Merge branch 'main' into model_monitoring_setup

fix: review comments
2026-09-26 22:51:56 +00:00 · 2022-08-25 08:22:05 -07:00 · 2022-08-25 15:21:03 +00:00 · 2022-08-25 15:18:55 +00:00 · 2022-08-25 15:08:56 +00:00 · 2022-08-25 15:08:07 +00:00
31 changed files with 920 additions and 834067 deletions
@@ -1,6 +1,5 @@
 *  @vertex-ai-samples-contributors  @GoogleCloudPlatform/cloudml-samples-owners
 /tf_agents_bandits_movie_recommendation_with_kfp_and_vertex_sdk	@yinghsienwu
-/pytorch_pre_built_images_deployment @googleapis/vertex-prediction-team
 /pytorch_text_classification_using_vertex_sdk_and_gcloud  @RajeshThallam
 /pytorch_text_classification_using_vertex_sdk_and_gcloud		@RajeshThallam @ultrons
 /sklearn_text_classification_from_script_using_vertex_sdk       @maxhardt
@@ -1,30 +0,0 @@
-# PyTorch Deployment on Google Cloud: Text Classification
-
-**This is an Experimental release**, covered by the Pre-GA Offerings Terms of your Google Cloud Platform [Terms of Service](https://cloud.google.com/terms).
-
-Experiments are focused on validating a prototype and are not guaranteed to be released. They are not intended for production use or covered by any SLA, support obligation, or deprecation policy and might be subject to backward-incompatible changes.
-
-**Kindly drop us a note before you run any scale tests.**
-
-**Do not hesitate to contact vertexai-prediction-preview-feedback@google.com if you have any questions or run into any issues.**
-
-The projects need to be added to the allowlist in order to deploy PyTorch models using Vertex AI Prediction pre-built PyTorch images. If you are interested in the feature, please send an email to vertexai-prediction-preview-feedback@google.com to provide your project numbers OR project ids.
-
-## Overview
-
-In the PyTorch on Google Cloud series of blog posts, we aim to share how to deploy PyTorch models at scale on [Vertex AI](https://cloud.google.com/vertex-ai).
-
-This tutorial on text classification shows how to deploy a PyTorch based text classification model on [Vertex AI](https://cloud.google.com/vertex-ai/docs/start/client-libraries#python) using Vertex SDK and [`gcloud ai`](https://cloud.google.com/sdk/gcloud/reference/beta/ai).
-
-## Notebooks
-
-| <h4>Notebook</h4> | <h4>Description</h4>                       |
-| :-------- | :------- |
-| [pytorch-text-classification-vertex-ai-deploy.ipynb](./pytorch-text-classification-vertex-ai-deploy.ipynb) | Notebook to show deploying a PyTorch model on Vertex AI |
-
-## Folders
-
-
-| <h4>Folder Name</h4> | <h4>Description</h4>                       |
-| :-------- | :------- |
-| [`predictor`](./predictor) | Folder with custom prediction handler to deploy a PyTorch model to Vertex Prediction. In the [notebook](./pytorch-text-classification-vertex-ai-deploy.ipynb), this folder is used for deploying a PyTorch model on Vertex AI using Vertex Prediction pre-built PyTorch images |
@@ -1,91 +0,0 @@
-
-import os
-import json
-import logging
-
-import torch
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-from ts.torch_handler.base_handler import BaseHandler
-
-logger = logging.getLogger(__name__)
-
-
-class TransformersClassifierHandler(BaseHandler):
-    """
-    The handler takes an input string and returns the classification text 
-    based on the serialized transformers checkpoint.
-    """
-    def __init__(self):
-        super(TransformersClassifierHandler, self).__init__()
-        self.initialized = False
-
-    def initialize(self, ctx):
-        """ Loads the model.pt file and initialized the model object.
-        Instantiates Tokenizer for preprocessor to use
-        Loads labels to name mapping file for post-processing inference response
-        """
-        self.manifest = ctx.manifest
-
-        properties = ctx.system_properties
-        model_dir = properties.get("model_dir")
-        self.device = torch.device("cuda:" + str(properties.get("gpu_id")) if torch.cuda.is_available() else "cpu")
-
-        # Read model serialize/pt file
-        serialized_file = self.manifest["model"]["serializedFile"]
-        model_pt_path = os.path.join(model_dir, serialized_file)
-        if not os.path.isfile(model_pt_path):
-            raise RuntimeError("Missing the model.pt or pytorch_model.bin file")
-        
-        # Load model
-        self.model = AutoModelForSequenceClassification.from_pretrained(model_dir)
-        self.model.to(self.device)
-        self.model.eval()
-        logger.debug('Transformer model from path {0} loaded successfully'.format(model_dir))
-        
-        # Ensure to use the same tokenizer used during training
-        self.tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
-
-        # Read the mapping file, index to object name
-        mapping_file_path = os.path.join(model_dir, "index_to_name.json")
-
-        if os.path.isfile(mapping_file_path):
-            with open(mapping_file_path) as f:
-                self.mapping = json.load(f)
-        else:
-            logger.warning('Missing the index_to_name.json file. Inference output will default.')
-            self.mapping = {"0": "Negative",  "1": "Positive"}
-
-        self.initialized = True
-
-    def preprocess(self, data):
-        """ Preprocessing input request by tokenizing
-            Extend with your own preprocessing steps as needed
-        """
-        text = data[0].get("data")
-        if text is None:
-            text = data[0].get("body")
-        sentences = text.decode('utf-8')
-        logger.info("Received text: '%s'", sentences)
-
-        # Tokenize the texts
-        tokenizer_args = ((sentences,))
-        inputs = self.tokenizer(*tokenizer_args,
-                                padding='max_length',
-                                max_length=128,
-                                truncation=True,
-                                return_tensors = "pt")
-        return inputs
-
-    def inference(self, inputs):
-        """ Predict the class of a text using a trained transformer model.
-        """
-        prediction = self.model(inputs['input_ids'].to(self.device))[0].argmax().item()
-
-        if self.mapping:
-            prediction = self.mapping[str(prediction)]
-
-        logger.info("Model predicted: '%s'", prediction)
-        return [prediction]
-
-    def postprocess(self, inference_output):
-        return inference_output
@@ -1,5 +0,0 @@
-
-{
-    "0": "Negative", 
-    "1": "Positive"
-}
@@ -658,8 +658,8 @@
      },
      "outputs": [],
      "source": [
-        "dataset = load_dataset(\"imdb\")\n",
-        "dataset"
+        "datasets = load_dataset(\"imdb\")\n",
+        "datasets"
      ]
    },
    {
@@ -668,7 +668,7 @@
        "id": "RzfPtOMoIrIu"
      },
      "source": [
-        "The `dataset` object itself is [`DatasetDict`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasetdict), which contains one key for the training, validation and test set."
+        "The `datasets` object itself is [`DatasetDict`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasetdict), which contains one key for the training, validation and test set."
      ]
    },
    {
@@ -681,12 +681,12 @@
      "source": [
        "print(\n",
        "    \"Total # of rows in training dataset {} and size {:5.2f} MB\".format(\n",
-        "        dataset[\"train\"].shape[0], dataset[\"train\"].size_in_bytes / (1024 * 1024)\n",
+        "        datasets[\"train\"].shape[0], datasets[\"train\"].size_in_bytes / (1024 * 1024)\n",
        "    )\n",
        ")\n",
        "print(\n",
        "    \"Total # of rows in test dataset {} and size {:5.2f} MB\".format(\n",
-        "        dataset[\"test\"].shape[0], dataset[\"test\"].size_in_bytes / (1024 * 1024)\n",
+        "        datasets[\"test\"].shape[0], datasets[\"test\"].size_in_bytes / (1024 * 1024)\n",
        "    )\n",
        ")"
      ]
@@ -708,7 +708,7 @@
      },
      "outputs": [],
      "source": [
-        "dataset[\"train\"][0]"
+        "datasets[\"train\"][0]"
      ]
    },
    {
@@ -728,7 +728,7 @@
      },
      "outputs": [],
      "source": [
-        "label_list = dataset[\"train\"].unique(\"label\")\n",
+        "label_list = datasets[\"train\"].unique(\"label\")\n",
        "label_list"
      ]
    },
@@ -779,7 +779,7 @@
      },
      "outputs": [],
      "source": [
-        "show_random_elements(dataset[\"train\"])"
+        "show_random_elements(datasets[\"train\"])"
      ]
    },
    {
@@ -883,7 +883,7 @@
      },
      "outputs": [],
      "source": [
-        "example = dataset[\"train\"][4]\n",
+        "example = datasets[\"train\"][4]\n",
        "print(example)"
      ]
    },
@@ -920,7 +920,7 @@
      "source": [
        "# Dataset loading repeated here to make this cell idempotent\n",
        "# Since we are over-writing datasets variable\n",
-        "dataset = load_dataset(\"imdb\")\n",
+        "datasets = load_dataset(\"imdb\")\n",
        "\n",
        "# Mapping labels to ids\n",
        "# NOTE: We can extract this automatically but the `Unique` method of the datasets\n",
@@ -948,7 +948,7 @@
        "\n",
        "\n",
        "# apply preprocessing function to input examples\n",
-        "dataset = dataset.map(preprocess_function, batched=True, load_from_cache_file=True)"
+        "datasets = datasets.map(preprocess_function, batched=True, load_from_cache_file=True)"
      ]
    },
    {
@@ -1091,8 +1091,8 @@
        "trainer = Trainer(\n",
        "    model,\n",
        "    args,\n",
-        "    train_dataset=dataset[\"train\"],\n",
-        "    eval_dataset=dataset[\"test\"],\n",
+        "    train_dataset=datasets[\"train\"],\n",
+        "    eval_dataset=datasets[\"test\"],\n",
        "    data_collator=default_data_collator,\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics,\n",
@@ -340,7 +340,7 @@
      "source": [
        "### Authenticate your Google Cloud account\n",
        "\n",
-        "**If you are using Vertex AI Workbench Notebooks**, your environment is already authenticated. \n",
+        "**If you are using Vertex AI Workbench Notebooks**, your environment is already authenticated. Skip this step.\n",
        "\n",
        "**If you are using Colab**, run the cell below and follow the instructions when prompted to authenticate your account via oAuth.\n",
        "\n",
@@ -376,11 +376,12 @@
        "import sys\n",
        "\n",
        "# If on Vertex AI Workbench, then don't execute this code\n",
-        "IS_COLAB = \"google.colab\" in sys.modules\n",
+        "IS_COLAB = False\n",
        "if not os.path.exists(\"/opt/deeplearning/metadata/env_version\") and not os.getenv(\n",
        "    \"DL_ANACONDA_HOME\"\n",
        "):\n",
        "    if \"google.colab\" in sys.modules:\n",
+        "        IS_COLAB = True\n",
        "        from google.colab import auth as google_auth\n",
        "\n",
        "        google_auth.authenticate_user()\n",
@@ -427,9 +428,8 @@
      },
      "outputs": [],
      "source": [
-        "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"[your-bucket-name]\":\n",
-        "    BUCKET_NAME = PROJECT_ID + \"aip-\" + TIMESTAMP\n",
-        "    BUCKET_URI = f\"gs://{BUCKET_NAME}\""
+        "if BUCKET_URI == \"\" or BUCKET_URI is None or BUCKET_URI == \"gs://[your-bucket-name]\":\n",
+        "    BUCKET_URI = \"gs://\" + PROJECT_ID + \"aip-\" + TIMESTAMP"
      ]
    },
    {
@@ -785,7 +785,7 @@
      },
      "outputs": [],
      "source": [
-        "print(endpoint.gca_resource)"
+        "endpoint.gca_resource"
      ]
    },
    {
@@ -908,7 +908,7 @@
      },
      "outputs": [],
      "source": [
-        "print(endpoint.gca_resource.deployed_models[0])"
+        "endpoint.gca_resource.deployed_models[0]"
      ]
    },
    {
@@ -1203,10 +1203,12 @@
        "\n",
        "In this pipeline, you create an `Endpoint` resource, and then you deploy a `Model` resource to the `Endpoint` resource. The `Model` resource  to deploy is your existing TFHub model which you previously imported as a `Model` resource. The steps are:\n",
        "\n",
-        "- For pipeline parameters, pass the resource name for the existing `Model` resource.\n",
-        "- Use the `GetVertexModelOp()` component to create a `VertexModel` pipeline artifact for the model.\n",
+        "- For pipeline parameters, pass the resource name and resource URI for the existing `Model` resource.\n",
+        "- Use the `importer_node()` component to create a `VertexModel` pipeline artifact for the model.\n",
        "- Create an `Endpoint` resource.\n",
-        "- Using the `VertexModel` pipeline artifact, deploy the `Model` resource to the `Endpoint` resource."
+        "- Using the `VertexModel` pipeline artifact, deploy the `Model` resource to the `Endpoint` resource.\n",
+        "\n",
+        "*Note:* This example currently blocked by internal issue: b/219835305"
      ]
    },
    {
@@ -1223,6 +1225,20 @@
        "\n",
        "PIPELINE_ROOT = \"{}/pipeline_root/endpoint_example\".format(BUCKET_URI)\n",
        "\n",
+        "# (WORKAROUND  b/219835305)\n",
+        "@component(\n",
+        "    base_image=\"python:3.9\",\n",
+        "    packages_to_install=[\"google-cloud-aiplatform\"],\n",
+        ")\n",
+        "def return_unmanaged_model(\n",
+        "    serving_image: str, artifact_uri: str, resource_name: str, model: Output[Artifact]\n",
+        "):\n",
+        "    model.metadata[\"containerSpec\"] = {\"imageUri\": serving_image}\n",
+        "\n",
+        "    model.metadata[\"resourceName\"] = resource_name\n",
+        "\n",
+        "    model.uri = artifact_uri\n",
+        "\n",
        "\n",
        "@dsl.pipeline(\n",
        "    name=\"create-endpoint-deploy-model\",\n",
@@ -1230,16 +1246,34 @@
        ")\n",
        "def pipeline(\n",
        "    display_name: str,\n",
+        "    resource_uri: str,\n",
        "    resource_name: str,\n",
+        "    # Model properties (WORKAROUND  b/219835305)\n",
+        "    serving_image: str,\n",
+        "    artifact_uri: str,\n",
        "    project: str = PROJECT_ID,\n",
        "    region: str = REGION,\n",
        "):\n",
-        "    from google_cloud_pipeline_components.experimental.evaluation import \\\n",
-        "        GetVertexModelOp\n",
+        "    from google_cloud_pipeline_components.types import artifact_types\n",
        "    from google_cloud_pipeline_components.v1.endpoint import (EndpointCreateOp,\n",
        "                                                              ModelDeployOp)\n",
+        "    from kfp.v2.components import importer_node\n",
        "\n",
-        "    model = GetVertexModelOp(model_resource_name=resource_name)\n",
+        "    # Desired sequence: blocked by b/219835305\n",
+        "    \"\"\"\n",
+        "    model = importer_node.importer(\n",
+        "        artifact_uri=resource_uri,\n",
+        "        artifact_class=artifact_types.VertexModel,\n",
+        "        metadata={\"resourceName\": resource_name},\n",
+        "    )\n",
+        "    \"\"\"\n",
+        "\n",
+        "    # (WORKAROUND  b/219835305)\n",
+        "    model = return_unmanaged_model(\n",
+        "        serving_image=serving_image,\n",
+        "        artifact_uri=artifact_uri,\n",
+        "        resource_name=resource_name,\n",
+        "    )\n",
        "\n",
        "    endpoint_op = EndpointCreateOp(\n",
        "        project=project,\n",
@@ -1247,7 +1281,7 @@
        "        display_name=display_name,\n",
        "    )\n",
        "\n",
-        "    _ = ModelDeployOp(\n",
+        "    deploy_op = ModelDeployOp(\n",
        "        model=model.outputs[\"model\"],\n",
        "        endpoint=endpoint_op.outputs[\"endpoint\"],\n",
        "        dedicated_resources_min_replica_count=1,\n",
@@ -1276,6 +1310,7 @@
        "\n",
        "- `display_name`: The display name for the generated Vertex AI resources.\n",
        "- `resource_name`: The resource name of the existing `Model` resource.\n",
+        "- `resource_uri`: The resource uri of the existing `Model` resource.\n",
        "- `project`: The project ID.\n",
        "- `region`: The region."
      ]
@@ -1288,6 +1323,10 @@
      },
      "outputs": [],
      "source": [
+        "# Model properties (WORKAROUND  b/219835305)\n",
+        "SERVING_CONTAINER_URI = model.gca_resource.container_spec.image_uri\n",
+        "ARTIFACT_URI = model.gca_resource.artifact_uri\n",
+        "\n",
        "try:\n",
        "    pipeline = aip.PipelineJob(\n",
        "        display_name=\"create-endpoint-deploy-pipeline\",\n",
@@ -1296,6 +1335,11 @@
        "        parameter_values={\n",
        "            \"display_name\": \"create_endpoint_and_deploy_model_\" + TIMESTAMP,\n",
        "            \"resource_name\": model.resource_name,\n",
+        "            \"resource_uri\": \"https://us-central1-aiplatform.googleapis.com/v1/\"\n",
+        "            + model.resource_name,\n",
+        "            # Model properties (WORKAROUND  b/219835305)\n",
+        "            \"serving_image\": SERVING_CONTAINER_URI,\n",
+        "            \"artifact_uri\": ARTIFACT_URI,\n",
        "            \"project\": PROJECT_ID,\n",
        "            \"region\": REGION,\n",
        "        },\n",
@@ -1444,7 +1488,7 @@
        "\n",
        "- For pipeline parameters, pass the resource names and resource URIs for the existing `Model` and `Endpoint` resource.\n",
        "- Use the `importer_node()` component to create a `VertexModel` pipeline artifact for the model.\n",
-        "- Use the `GetVertexModelOp()` component to create a `VertexModel` pipeline artifact for the model.\n",
+        "- Use the `importer_node()` component to create a `VertexEndpoint` pipeline artifact for the endpoint.\n",
        "- Using the `VertexModel` and `VertexEndpoint` pipeline artifacts, deploy the `Model` resource to the `Endpoint` resource.\n",
        "\n",
        "*Note:* This example currently blocked by internal issue: b/219835305"
@@ -1460,7 +1504,6 @@
      "source": [
        "PIPELINE_ROOT = \"{}/pipeline_root/endpoint_example_2\".format(BUCKET_URI)\n",
        "\n",
-        "\n",
        "# (WORKAROUND  b/219835305)\n",
        "@component(\n",
        "    base_image=\"python:3.9\",\n",
@@ -1477,23 +1520,35 @@
        ")\n",
        "def pipeline(\n",
        "    display_name: str,\n",
+        "    model_resource_uri: str,\n",
        "    model_resource_name: str,\n",
        "    endpoint_resource_uri: str,\n",
        "    endpoint_resource_name: str,\n",
+        "    # Model properties (WORKAROUND  b/219835305)\n",
+        "    serving_image: str,\n",
+        "    artifact_uri: str,\n",
        "    project: str = PROJECT_ID,\n",
        "    region: str = REGION,\n",
        "):\n",
-        "    from google_cloud_pipeline_components.experimental.evaluation import \\\n",
-        "        GetVertexModelOp\n",
+        "    from google_cloud_pipeline_components.types import artifact_types\n",
        "    from google_cloud_pipeline_components.v1.endpoint import ModelDeployOp\n",
+        "    from kfp.v2.components import importer_node\n",
        "\n",
        "    # Desired sequence: blocked by b/219835305\n",
        "    \"\"\"\n",
-        "    from kfp.v2.components import importer_node\n",
-        "    from google_cloud_pipeline_components.types import artifact_types\n",
+        "    model = importer_node.importer(\n",
+        "        artifact_uri=resource_uri,\n",
+        "        artifact_class=artifact_types.VertexModel,\n",
+        "        metadata={\"resourceName\": resource_name},\n",
+        "    )\n",
        "    \"\"\"\n",
        "\n",
-        "    model = GetVertexModelOp(model_resource_name=model_resource_name)\n",
+        "    # (WORKAROUND  b/219835305)\n",
+        "    model = return_unmanaged_model(\n",
+        "        serving_image=serving_image,\n",
+        "        artifact_uri=artifact_uri,\n",
+        "        resource_name=model_resource_name,\n",
+        "    )\n",
        "\n",
        "    # Desired sequence: blocked by b/219835305\n",
        "    \"\"\"\n",
@@ -1507,7 +1562,7 @@
        "    # (WORKAROUND  b/219835305)\n",
        "    endpoint = return_unmanaged_endpoint(resource_name=endpoint_resource_name)\n",
        "\n",
-        "    _ = ModelDeployOp(\n",
+        "    deploy_op = ModelDeployOp(\n",
        "        model=model.outputs[\"model\"],\n",
        "        endpoint=endpoint.outputs[\"endpoint\"],\n",
        "        dedicated_resources_min_replica_count=1,\n",
@@ -1536,6 +1591,7 @@
        "\n",
        "- `display_name`: The display name for the generated Vertex AI resources.\n",
        "- `model_resource_name`: The resource name of the existing `Model` resource.\n",
+        "- `model_resource_uri`: The resource uri of the existing `Model` resource.\n",
        "- `endpoint_resource_name`: The resource name of the existing `Endpoint` resource.\n",
        "- `endpoint_resource_uri`: The resource uri of the existing `Endpoint` resource.\n",
        "- `project`: The project ID.\n",
@@ -1558,9 +1614,14 @@
        "        parameter_values={\n",
        "            \"display_name\": \"deploy_model_existing_endpoint_\" + TIMESTAMP,\n",
        "            \"model_resource_name\": model.resource_name,\n",
+        "            \"model_resource_uri\": \"https://us-central1-aiplatform.googleapis.com/v1/\"\n",
+        "            + model.resource_name,\n",
        "            \"endpoint_resource_name\": endpoint.resource_name,\n",
        "            \"endpoint_resource_uri\": \"https://us-central1-aiplatform.googleapis.com/v1/\"\n",
        "            + endpoint.resource_name,\n",
+        "            # Model properties (WORKAROUND  b/219835305)\n",
+        "            \"serving_image\": SERVING_CONTAINER_URI,\n",
+        "            \"artifact_uri\": ARTIFACT_URI,\n",
        "            \"project\": PROJECT_ID,\n",
        "            \"region\": REGION,\n",
        "        },\n",
@@ -385,11 +385,12 @@
        "import sys\n",
        "\n",
        "# If on Vertex AI Workbench, then don't execute this code\n",
-        "IS_COLAB = \"google.colab\" in sys.modules\n",
+        "IS_COLAB = False\n",
        "if not os.path.exists(\"/opt/deeplearning/metadata/env_version\") and not os.getenv(\n",
        "    \"DL_ANACONDA_HOME\"\n",
        "):\n",
        "    if \"google.colab\" in sys.modules:\n",
+        "        IS_COLAB = True\n",
        "        from google.colab import auth as google_auth\n",
        "\n",
        "        google_auth.authenticate_user()\n",
@@ -1067,6 +1068,7 @@
        "- `model`: The `Model` resource.\n",
        "- `deployed_model_displayed_name`: The human readable name for the deployed model instance.\n",
        "- `machine_type`: The machine type for each VM instance.\n",
+        "- `traffic_split`: Set to `{}` to indicate no traffic split.\n",
        "\n",
        "Do to the requirements to provision the resource, this may take upto a few minutes."
      ]
@@ -1083,6 +1085,7 @@
        "    model=model,\n",
        "    deployed_model_display_name=\"example_\" + TIMESTAMP,\n",
        "    machine_type=DEPLOY_COMPUTE,\n",
+        "    traffic_split={},  # no traffic split\n",
        ")\n",
        "\n",
        "print(endpoint)"
@@ -1184,6 +1187,62 @@
        "    f.write(json.dumps({\"instances\": [{serving_input: {\"b64\": b64str}}]}))"
      ]
    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "23e995c35fd6"
+      },
+      "source": [
+        "#### Construct the `Private Endpoint` URI\n",
+        "\n",
+        "Next, you construct the URI for the `Private Endpoint`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "97b248b2efb5"
+      },
+      "outputs": [],
+      "source": [
+        "endpoint_id = endpoint.resource_name\n",
+        "\n",
+        "ENDPOINT_URL = ! gcloud beta ai endpoints describe {endpoint_id} \\\n",
+        "  --region={REGION} \\\n",
+        "  --format=\"value(deployedModels.privateEndpoints.predictHttpUri)\"\n",
+        "\n",
+        "private_url = ENDPOINT_URL[1]\n",
+        "print(private_url)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "27605b5f0c3a"
+      },
+      "source": [
+        "### Make the prediction request using curl\n",
+        "\n",
+        "Use `curl` to make the prediction request to the private URI."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6cb568e6bb49"
+      },
+      "outputs": [],
+      "source": [
+        "output = ! curl -X POST -d@instances.json $private_url\n",
+        "\n",
+        "predictions = output[5]\n",
+        "print(predictions)\n",
+        "\n",
+        "! rm test.jpg instances.json"
+      ]
+    },
    {
      "cell_type": "markdown",
      "metadata": {
@@ -1192,7 +1251,7 @@
      "source": [
        "### Make the prediction request using SDK\n",
        "\n",
-        "Next, use the `Vertex AI SDK` to make a prediction request."
+        "Finally, use the `Vertex AI SDK` to make a prediction request."
      ]
    },
    {
@@ -702,7 +702,7 @@
        "        + f\"/{PRIVATE_REPO}\"\n",
        "        + \"/tf_serving:gpu\"\n",
        "    )\n",
-        "    TF_IMAGE = \"tensorflow/serving:2.5.4-gpu\"\n",
+        "    TF_IMAGE = \"tensorflow/serving:latest-gpu\"\n",
        "else:\n",
        "    DEPLOY_IMAGE = (\n",
        "        f\"{REGION}-docker.pkg.dev/\"\n",
@@ -710,15 +710,15 @@
        "        + f\"/{PRIVATE_REPO}\"\n",
        "        + \"/tf_serving:cpu\"\n",
        "    )\n",
-        "    TF_IMAGE = \"tensorflow/serving:2.5.4\"\n",
+        "    TF_IMAGE = \"tensorflow/serving:latest\"\n",
        "\n",
        "if not IS_COLAB:\n",
        "    if DEPLOY_GPU:\n",
-        "        ! sudo docker pull tensorflow/serving:2.5.4-gpu\n",
+        "        ! sudo docker pull tensorflow/serving:latest-gpu\n",
        "    else:\n",
-        "        ! sudo docker pull tensorflow/serving:2.5.4\n",
+        "        ! sudo docker pull tensorflow/serving:latest\n",
        "\n",
-        "    ! docker tag $TF_IMAGE $DEPLOY_IMAGE\n",
+        "    ! docker tag tensorflow/serving $DEPLOY_IMAGE\n",
        "    ! docker push $DEPLOY_IMAGE\n",
        "else:\n",
        "    # install docker daemon\n",
@@ -1434,7 +1434,7 @@
      },
      "outputs": [],
      "source": [
-        "delete_bucket = True\n",
+        "delete_bucket = False\n",
        "delete_model = True\n",
        "delete_endpoint = True\n",
        "delete_batch_job = True\n",
@@ -766,7 +766,7 @@
      "source": [
        "## Introduction to Vertex AI Model Monitoring\n",
        "\n",
-        "Vertex AI Model Monitoring is supported for AutoML tabular models and custom tabular models. You can monitor for skew and drift detection of the features in the inbound prediction requests or skew and drift detection of the feature attributions (Explainable AI) in the outbound prediction response -- that is, the distribution of the attributions on how they contributed to the output (predictions).\n",
+        "Vertex AI Model Monitoring is supported for AutoML tabular models and custom tabular model. You can monitor for skew and drift detection of the features in the inbound prediction requests or skew and drift detection of the feature attributions (Explainable AI) in the outbound prediction response -- that is, the distribution of the attributions on how they contributed to the output (predictions).\n",
        "\n",
        "The following are the basic steps to enable model monitoring:\n",
        "\n",
@@ -781,7 +781,7 @@
        "\n",
        "When model monitoring is enabled, the sampled incoming prediction requests are logged into a BigQuery table. The input feature values contained in the logged requests are then analyzed for skew or drift on an specified interval basis. You set a sampling rate to monitor a subset of the production inputs to a model, and the monitoring interval.\n",
        "\n",
-        "The model monitoring service needs to know how to parse the feature values, which is referred to as the input schema. For AutoML tabular models, the input schema is automatically generated. For custom tabular models, the service will attempt to automatically derive the input schema from the first 1000 prediction requests. Alternatively, one can upload the input schema.\n",
+        "The model monitoring service needs to know how to parse the feature values, which is referred to as the input schema. For AutoML tabular models, the input schema is automatically provided. For custom tabular models, the service will attempt to automatically derive the input schema from the first 1000 prediction requests. Alternatively, one can upload the input schema.\n",
        "\n",
        "For skew detection, the monitoring service requires a baseline for the statistical distribution of values in the training data. For AutoML tabular models this is automatically derived. For custom tabular models, you upload the training data to the service, and have the service automatically derive the distribution.\n",
        "\n",
@@ -30,4 +30,3 @@
 /custom/custom_training_tensorboard_profiler.ipynb @itseric
 /workbench/spark/spark_sample_notebook.ipynb @bmiro
 /model-registry/bqml-vertexai-model-registry.ipynb @soheilazangeneh
-/workbench/exploratory_data_analysis/explore_data_in_bigquery_with_workbench.ipynb @alokpattani
@@ -176,10 +176,7 @@
        "else:\n",
        "    USER_FLAG = \"\"\n",
        "\n",
-        "! (pip3 install --upgrade $USER_FLAG \\\n",
-        "    google-cloud-bigquery[pandas]==2.34.4 \\\n",
-        "    google-cloud-aiplatform==1.16.1 \\\n",
-        "    google-cloud-pipeline-components==1.0.18)"
+        "! pip3 install --upgrade google-cloud-bigquery[pandas] google-cloud-aiplatform google-cloud-pipeline-components $USER_FLAG"
      ]
    },
    {
@@ -8,7 +8,7 @@
      },
      "outputs": [],
      "source": [
-        "# Copyright 2021 Google LLC\n",
+        "# Copyright 2022 Google LLC\n",
        "#\n",
        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
@@ -66,6 +66,17 @@
        "This tutorial demonstrates how to use the Vertex AI SDK to create image object detection models and do batch prediction using a Google Cloud [AutoML](https://cloud.google.com/vertex-ai/docs/start/automl-users) model."
      ]
    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dataset:salads,iod"
+      },
+      "source": [
+        "### Dataset\n",
+        "\n",
+        "The dataset used for this tutorial is the Salads category of the [OpenImages dataset](https://www.tensorflow.org/datasets/catalog/open_images_v4) from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview). This dataset does not require any feature engineering. The version of the dataset you will use in this tutorial is stored in a public Cloud Storage bucket. The trained model predicts the bounding box locations and the corresponding type of salad items in an image from a class of five items: salad, seafood, tomato, baked goods, or cheese."
+      ]
+    },
    {
      "cell_type": "markdown",
      "metadata": {
@@ -90,17 +101,6 @@
        "* Batch Prediction Service: Does a queued (batch) prediction for the entire set of instances in the background and stores the results in a Cloud Storage bucket when ready."
      ]
    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dataset:salads,iod"
-      },
-      "source": [
-        "### Dataset\n",
-        "\n",
-        "The dataset used for this tutorial is the Salads category of the [OpenImages dataset](https://www.tensorflow.org/datasets/catalog/open_images_v4) from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview). This dataset does not require any feature engineering. The version of the dataset you will use in this tutorial is stored in a public Cloud Storage bucket. The trained model predicts the bounding box locations and the corresponding type of salad items in an image from a class of five items: salad, seafood, tomato, baked goods, or cheese."
-      ]
-    },
    {
      "cell_type": "markdown",
      "metadata": {
@@ -201,7 +201,7 @@
      },
      "outputs": [],
      "source": [
-        "! pip3 install -U --upgrade tensorflow google-cloud-storage $USER_FLAG"
+        "! pip3 install -U google-cloud-storage $USER_FLAG"
      ]
    },
    {
@@ -213,6 +213,17 @@
        "Install the latest version of *tensorflow* library."
      ]
    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "install_tensorflow"
+      },
+      "outputs": [],
+      "source": [
+        "! pip3 install --upgrade tensorflow $USER_FLAG"
+      ]
+    },
    {
      "cell_type": "markdown",
      "metadata": {
@@ -372,9 +383,9 @@
        "id": "timestamp"
      },
      "source": [
-        "#### UUID\n",
+        "#### Timestamp\n",
        "\n",
-        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
+        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append the timestamp onto the name of resources you create in this tutorial."
      ]
    },
    {
@@ -385,16 +396,9 @@
      },
      "outputs": [],
      "source": [
-        "import random\n",
-        "import string\n",
+        "from datetime import datetime\n",
        "\n",
-        "\n",
-        "# Generate a uuid of a specifed length(default=8)\n",
-        "def generate_uuid(length: int = 8) -> str:\n",
-        "    return \"\".join(random.choices(string.ascii_lowercase + string.digits, k=length))\n",
-        "\n",
-        "\n",
-        "UUID = generate_uuid()"
+        "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
      ]
    },
    {
@@ -405,7 +409,7 @@
      "source": [
        "### Authenticate your Google Cloud account\n",
        "\n",
-        "**If you are using Google Cloud Notebooks**, your environment is already authenticated.\n",
+        "**If you are using Google Cloud Notebooks**, your environment is already authenticated. Skip this step.\n",
        "\n",
        "**If you are using Colab**, run the cell below and follow the instructions when prompted to authenticate your account via oAuth.\n",
        "\n",
@@ -490,7 +494,7 @@
      "outputs": [],
      "source": [
        "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"[your-bucket-name]\":\n",
-        "    BUCKET_URI = \"gs://\" + PROJECT_ID + \"aip-\" + UUID"
+        "    BUCKET_URI = \"gs://\" + PROJECT_ID + \"aip-\" + TIMESTAMP"
      ]
    },
    {
@@ -665,7 +669,7 @@
      "outputs": [],
      "source": [
        "dataset = aiplatform.ImageDataset.create(\n",
-        "    display_name=\"Salads\" + \"_\" + UUID,\n",
+        "    display_name=\"Salads\" + \"_\" + TIMESTAMP,\n",
        "    gcs_source=[IMPORT_FILE],\n",
        "    import_schema_uri=aiplatform.schema.dataset.ioformat.image.bounding_box,\n",
        ")\n",
@@ -713,7 +717,7 @@
      "outputs": [],
      "source": [
        "job = aiplatform.AutoMLImageTrainingJob(\n",
-        "    display_name=\"salads_\" + UUID,\n",
+        "    display_name=\"salads_\" + TIMESTAMP,\n",
        "    prediction_type=\"object_detection\",\n",
        "    multi_label=False,\n",
        "    model_type=\"CLOUD\",\n",
@@ -756,7 +760,7 @@
      "source": [
        "model = job.run(\n",
        "    dataset=dataset,\n",
-        "    model_display_name=\"salads_\" + UUID,\n",
+        "    model_display_name=\"salads_\" + TIMESTAMP,\n",
        "    training_fraction_split=0.8,\n",
        "    validation_fraction_split=0.1,\n",
        "    test_fraction_split=0.1,\n",
@@ -786,7 +790,7 @@
      "outputs": [],
      "source": [
        "# Get model resource ID\n",
-        "models = aiplatform.Model.list(filter=\"display_name=salads_\" + UUID)\n",
+        "models = aiplatform.Model.list(filter=\"display_name=salads_\" + TIMESTAMP)\n",
        "\n",
        "# Get a reference to the Model Service client\n",
        "client_options = {\"api_endpoint\": f\"{REGION}-aiplatform.googleapis.com\"}\n",
@@ -957,7 +961,7 @@
      "outputs": [],
      "source": [
        "batch_predict_job = model.batch_predict(\n",
-        "    job_display_name=\"salads_\" + UUID,\n",
+        "    job_display_name=\"salads_\" + TIMESTAMP,\n",
        "    gcs_source=gcs_input_uri,\n",
        "    gcs_destination_prefix=BUCKET_URI,\n",
        "    machine_type=\"n1-standard-4\",\n",
@@ -44,7 +44,7 @@
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/custom/custom-tabular-bq-managed-dataset.ipynb\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/custom/custom-tabular-bq-managed-dataset.ipynb\">\n",
        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
        "      Open in Vertex AI Workbench\n",
        "    </a>\n",
@@ -8,7 +8,7 @@
      },
      "outputs": [],
      "source": [
-        "# Copyright 2021 Google LLC\n",
+        "# Copyright 2022 Google LLC\n",
        "#\n",
        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
@@ -54,31 +54,13 @@
    {
      "cell_type": "markdown",
      "metadata": {
-        "id": "d975c5729f18"
+        "id": "tvgnzT1CKxrO"
      },
      "source": [
        "## Overview\n",
        "\n",
-        "As a Data Scientist, you want to be able to reuse code path (data preprocessing, feature engineering etc...) that others within your team have written to simplify and standardize all the complex data wrangling. "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "3a0f8061b9c1"
-      },
-      "source": [
-        "### Objective\n",
+        "As a Data Scientist, you want to be able to reuse code path (data preprocessing, feature engineering etc...) that others within your team have written to simplify and standardize all the complex data wrangling. \n",
        "\n",
-        "In this notebook, you learn how to integrate preprocessing code in a Vertex AI experiments. Also you build the experiment lineage lets you record, analyze, debug, and audit metadata and artifacts produced along your ML journey."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "tvgnzT1CKxrO"
-      },
-      "source": [
        "### Dataset\n",
        "\n",
        "This dataset is the UCI News Aggregator Data Set which contains 422,937 news collected between March 10th, 2014 and August 10th, 2014. Below are example records from the dataset:\n",
@@ -90,15 +72,13 @@
        "|2  |Fed's Charles Plosser sees high bar for change in pace of tapering   |http://www.livemint.com/Politics/H2EvwJSK2VE6OF7iK1g3PP/Feds-Charles-Plosser-sees-high-bar-for-change-in-pace-of-ta.html   |Livemint         |b       |ddUyU0VZz0BRneMioxUPQVP6sIxvM|www.livemint.com   |1394470371207|\n",
        "|3  |US open: Stocks fall after Fed official hints at accelerated tapering|http://www.ifamagazine.com/news/us-open-stocks-fall-after-fed-official-hints-at-accelerated-tapering-294436                |IFA Magazine     |b       |ddUyU0VZz0BRneMioxUPQVP6sIxvM|www.ifamagazine.com|1394470371550|\n",
        "|4  |Fed risks falling 'behind the curve', Charles Plosser says           |http://www.ifamagazine.com/news/fed-risks-falling-behind-the-curve-charles-plosser-says-294430                             |IFA Magazine     |b       |ddUyU0VZz0BRneMioxUPQVP6sIxvM|www.ifamagazine.com|1394470371793|\n",
-        "|5  |Fed's Plosser: Nasty Weather Has Curbed Job Growth                   |http://www.moneynews.com/Economy/federal-reserve-charles-plosser-weather-job-growth/2014/03/10/id/557011                   |Moneynews        |b       |ddUyU0VZz0BRneMioxUPQVP6sIxvM|www.moneynews.com  |1394470372027|"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "5e2eba58ad71"
-      },
-      "source": [
+        "|5  |Fed's Plosser: Nasty Weather Has Curbed Job Growth                   |http://www.moneynews.com/Economy/federal-reserve-charles-plosser-weather-job-growth/2014/03/10/id/557011                   |Moneynews        |b       |ddUyU0VZz0BRneMioxUPQVP6sIxvM|www.moneynews.com  |1394470372027|\n",
+        "\n",
+        "\n",
+        "### Objective\n",
+        "\n",
+        "In this notebook, you learn how to integrate preprocessing code in a Vertex AI experiments. Also you will build the experiment lineage lets you record, analyze, debug, and audit metadata and artifacts produced along your ML journey.\n",
+        "\n",
        "### Costs \n",
        "\n",
        "This tutorial uses billable components of Google Cloud:\n",
@@ -170,7 +150,7 @@
      "source": [
        "### Install additional packages\n",
        "\n",
-        "Install additional package dependencies not installed in your notebook environment,TensorFlow or Vertex AI SDK. Use the latest major GA version of each package."
+        "Install additional package dependencies not installed in your notebook environment, such as TensorFlow or Vertex AI SDK. Use the latest major GA version of each package."
      ]
    },
    {
@@ -195,7 +175,7 @@
        "    USER_FLAG = \"--user\"\n",
        "\n",
        "! pip3 install {USER_FLAG} --upgrade joblib fsspec gcsfs scikit-learn -q\n",
-        "! pip install {USER_FLAG} --upgrade google-cloud-aiplatform -q"
+        "! pip3 install {USER_FLAG} --force-reinstall 'google-cloud-aiplatform>=1.15' -q"
      ]
    },
    {
@@ -228,14 +208,21 @@
        "    app.kernel.do_shutdown(True)"
      ]
    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lWEdiXsJg0XY"
+      },
+      "source": [
+        "## Before you begin"
+      ]
+    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BF1j6f9HApxa"
      },
      "source": [
-        "## Before you begin\n",
-        "\n",
        "### Set up your Google Cloud project\n",
        "\n",
        "**The following steps are required, regardless of your notebook environment.**\n",
@@ -246,7 +233,7 @@
        "\n",
        "1. [Enable APIs](https://console.cloud.google.com/flows/enableapi?apiid=cloudresourcemanager.googleapis.com,aiplatform.googleapis.com).\n",
        "\n",
-        "1. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk).\n",
+        "1. If you are running this notebook locally, you will need to install the [Cloud SDK](https://cloud.google.com/sdk).\n",
        "\n",
        "1. Enter your project ID in the cell below. Then run the cell to make sure the\n",
        "Cloud SDK uses the right project for all the commands in this notebook.\n",
@@ -356,9 +343,9 @@
        "id": "06571eb4063b"
      },
      "source": [
-        "#### UUID\n",
+        "#### Timestamp\n",
        "\n",
-        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
+        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append it onto the name of resources you create in this tutorial."
      ]
    },
    {
@@ -369,16 +356,9 @@
      },
      "outputs": [],
      "source": [
-        "import random\n",
-        "import string\n",
+        "from datetime import datetime\n",
        "\n",
-        "\n",
-        "# Generate a uuid of a specifed length(default=8)\n",
-        "def generate_uuid(length: int = 8) -> str:\n",
-        "    return \"\".join(random.choices(string.ascii_lowercase + string.digits, k=length))\n",
-        "\n",
-        "\n",
-        "UUID = generate_uuid()"
+        "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
      ]
    },
    {
@@ -390,7 +370,7 @@
        "### Authenticate your Google Cloud account\n",
        "\n",
        "**If you are using Vertex AI Workbench Notebooks**, your environment is already\n",
-        "authenticated."
+        "authenticated. Skip this step."
      ]
    },
    {
@@ -504,7 +484,7 @@
      "outputs": [],
      "source": [
        "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"[your-bucket-name]\":\n",
-        "    BUCKET_NAME = PROJECT_ID + \"aip-\" + UUID\n",
+        "    BUCKET_NAME = PROJECT_ID + \"aip-\" + TIMESTAMP\n",
        "    BUCKET_URI = f\"gs://{BUCKET_NAME}\""
      ]
    },
@@ -557,6 +537,17 @@
        "### Set project folder"
      ]
    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "AARD6Fsr-DSi"
+      },
+      "outputs": [],
+      "source": [
+        "DATA_PATH = \"data\""
+      ]
+    },
    {
      "cell_type": "code",
      "execution_count": null,
@@ -565,7 +556,6 @@
      },
      "outputs": [],
      "source": [
-        "DATA_PATH = \"data\"\n",
        "!mkdir -m 777 -p {DATA_PATH}"
      ]
    },
@@ -578,6 +568,17 @@
        "### Get the data"
      ]
    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3V6W2nIo9FtL"
+      },
+      "outputs": [],
+      "source": [
+        "DATASET_URL = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00359/NewsAggregatorDataset.zip\""
+      ]
+    },
    {
      "cell_type": "code",
      "execution_count": null,
@@ -586,7 +587,6 @@
      },
      "outputs": [],
      "source": [
-        "DATASET_URL = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00359/NewsAggregatorDataset.zip\"\n",
        "!wget --no-parent {DATASET_URL} --directory-prefix={DATA_PATH}\n",
        "!mkdir -m 777 -p {DATA_PATH}/temp {DATA_PATH}/raw\n",
        "!unzip {DATA_PATH}/*.zip -d {DATA_PATH}/temp\n",
@@ -662,7 +662,7 @@
        "# Experiments\n",
        "TASK = \"classification\"\n",
        "MODEL_TYPE = \"naivebayes\"\n",
-        "EXPERIMENT_NAME = f\"{TASK}-{MODEL_TYPE}-{UUID}\"\n",
+        "EXPERIMENT_NAME = f\"{TASK}-{MODEL_TYPE}-{TIMESTAMP}\"\n",
        "EXPERIMENT_RUN_NAME = \"run-1\"\n",
        "\n",
        "# Preprocessing\n",
@@ -690,7 +690,7 @@
        "FEATURES = \"title\"\n",
        "TEST_SIZE = 0.2\n",
        "SEED = 8\n",
-        "TRAINED_MODEL_URI = f\"{BUCKET_URI}/deliverables/{UUID}\"\n",
+        "TRAINED_MODEL_URI = f\"{BUCKET_URI}/deliverables/{TIMESTAMP}\"\n",
        "MODEL_NAME = f\"{EXPERIMENT_NAME}-model\""
      ]
    },
@@ -800,7 +800,7 @@
      "source": [
        "#### Create a Dataset Metadata Artifact\n",
        "\n",
-        "First you create the Dataset artifact to track the dataset resource in the Vertex ML Metadata and create the experiment lineage."
+        "First you create the Dataset artifact to track the dataset resource in the Vertex AI ML Metadata and create the experiment lineage."
      ]
    },
    {
@@ -839,6 +839,7 @@
        "Preprocess module\n",
        "\"\"\"\n",
        "\n",
+        "import string\n",
        "\n",
        "import pandas as pd\n",
        "\n",
@@ -868,10 +869,7 @@
      "source": [
        "#### Add the `preprocessing` Execution\n",
        "\n",
-        "Vertex AI Experiments supports tracking both executions and artifacts. Executions are steps in an ML workflow that can include but are not limited to data preprocessing, training, and model evaluation. Executions can consume artifacts such as datasets and produce artifacts such as models.\n",
-        "\n",
-        "You add the preprocessing step to track its execution in the lineage associated to Vertex AI Experiment. \n",
-        "For Vertex AI, the parameters are passed inside the message field which we see in the logs. These structures of the logs are predefined."
+        "You add the preprocessing step to track its execution in the lineage associated to Vertex AI Experiment. "
      ]
    },
    {
@@ -945,16 +943,7 @@
      "source": [
        "#### Create model training module\n",
        "\n",
-        "Below the training module.\n",
-        "\n",
-        "**get_training_split :** It takes parameters like  x(The data to be split), y(The labels to be split), test_size(The proportion of the data to be reserved for testing) and random_state(The seed used by the random number generator).\n",
-        "This function return training data, testing data , The training labels and The testing labels.\n",
-        "\n",
-        "**get_pipeline :** It return's the model.\n",
-        "\n",
-        "**train_pipeline :** It train the model by using model, training data, training lables and return's the trained model.\n",
-        "\n",
-        "**evaluate_model :** It evaluate the model and return the accuracy of the model.\n"
+        "Below the training module."
      ]
    },
    {
@@ -1162,15 +1151,6 @@
        "    exc.assign_output_artifacts([model])"
      ]
    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "e595c893de8d"
-      },
-      "source": [
-        "### Stop Experiment run"
-      ]
-    },
    {
      "cell_type": "code",
      "execution_count": null,
@@ -1190,7 +1170,7 @@
      "source": [
        "### Visualize Experiment Lineage\n",
        "\n",
-        "Below you get the link to Vertex AI Metadata UI in the console that show the experiment lineage."
+        "Below you will get the link to Vertex AI Metadata UI in the console that will show the experiment lineage."
      ]
    },
    {
@@ -1228,8 +1208,17 @@
      "source": [
        "# Delete experiment\n",
        "exp = vertex_ai.Experiment(EXPERIMENT_NAME)\n",
-        "exp.delete()\n",
-        "\n",
+        "exp.delete()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "gW8Ddbr8xaKp"
+      },
+      "outputs": [],
+      "source": [
        "# Delete model\n",
        "model_list = vertex_ai.Model.list(filter=f'display_name=\"{MODEL_NAME}\"')\n",
        "for model in model_list:\n",
@@ -1241,15 +1230,22 @@
        "        filter=f'display_name=\"{dataset_name}\"'\n",
        "    )\n",
        "    for dataset in dataset_list:\n",
-        "        dataset.delete()\n",
-        "\n",
+        "        dataset.delete()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "sx_vKniMq9ZX"
+      },
+      "outputs": [],
+      "source": [
        "# Delete Cloud Storage objects that were created\n",
        "delete_bucket = True\n",
        "\n",
        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
-        "    ! gsutil -m rm -r $BUCKET_URI\n",
-        "\n",
-        "!rm -Rf {DATA_PATH}"
+        "    ! gsutil -m rm -r $BUCKET_URI"
      ]
    },
    {
@@ -105,7 +105,7 @@
      "source": [
        "### Dataset\n",
        "\n",
-        "The dataset used for this tutorial is the Bank Marketing. This dataset does not require any feature engineering. The version of the dataset you use in this tutorial is stored in a public Cloud Storage bucket."
+        "The dataset used for this tutorial is the [Bank Marketing](https://pantheon.corp.google.com/storage/browser/_details/cloud-ml-tables-data/bank-marketing.csv) . This dataset does not require any feature engineering. The version of the dataset you use in this tutorial is stored in a public Cloud Storage bucket."
      ]
    },
    {
@@ -292,37 +292,6 @@
        "    PROJECT_ID = \"python-docs-samples-tests\"  # @param {type:\"string\"}"
      ]
    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "d9f118b92c74"
-      },
-      "source": [
-        "#### UUID\n",
-        "\n",
-        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "3ee72715c0fd"
-      },
-      "outputs": [],
-      "source": [
-        "import random\n",
-        "import string\n",
-        "\n",
-        "\n",
-        "# Generate a uuid of a specifed length(default=8)\n",
-        "def generate_uuid(length: int = 8) -> str:\n",
-        "    return \"\".join(random.choices(string.ascii_lowercase + string.digits, k=length))\n",
-        "\n",
-        "\n",
-        "UUID = generate_uuid()"
-      ]
-    },
    {
      "cell_type": "markdown",
      "metadata": {
@@ -509,6 +478,7 @@
        "from google.cloud.aiplatform_v1.types import \\\n",
        "    featurestore_service as featurestore_service_pb2\n",
        "from google.cloud.aiplatform_v1.types import io as io_pb2\n",
+        "from google.protobuf.duration_pb2 import Duration\n",
        "\n",
        "# Create admin_client for CRUD and data_client for reading feature values.\n",
        "admin_client = FeaturestoreServiceClient(client_options={\"api_endpoint\": API_ENDPOINT})\n",
@@ -572,7 +542,7 @@
      },
      "outputs": [],
      "source": [
-        "FEATURESTORE_ID = f\"movie_prediction_{UUID}\"\n",
+        "FEATURESTORE_ID = \"movie_prediction\"\n",
        "try:\n",
        "    create_lro = admin_client.create_featurestore(\n",
        "        featurestore_service_pb2.CreateFeaturestoreRequest(\n",
@@ -597,7 +567,7 @@
        "id": "ag8pCQ7rNjVf"
      },
      "source": [
-        "You can use [GetFeaturestore](https://cloud.google.com/vertex-ai/docs/reference/rpc/google.cloud.aiplatform.v1#google.cloud.aiplatform.v1.FeaturestoreService.GetFeaturestore) or [ListFeaturestores](https://cloud.google.com/vertex-ai/docs/reference/rpc/google.cloud.aiplatform.v1#google.cloud.aiplatform.v1.FeaturestoreService.ListFeaturestores) to check if the Featurestore was successfully created. The following example gets the details of the Featurestore.\n"
+        "You can use [GetFeaturestore](https://cloud.google.com/vertex-ai/docs/reference/rpc/google.cloud.aiplatform.v1beta1#google.cloud.aiplatform.v1beta1.FeaturestoreService.GetFeaturestore) or [ListFeaturestores](https://cloud.google.com/vertex-ai/docs/reference/rpc/google.cloud.aiplatform.v1beta1#google.cloud.aiplatform.v1beta1.FeaturestoreService.ListFeaturestores) to check if the Featurestore was successfully created. The following example gets the details of the Featurestore.\n"
      ]
    },
    {
@@ -619,7 +589,7 @@
        "id": "018ab19d934f"
      },
      "source": [
-        "Auto scaling is available in v1 since v1.11. Below is the example for the `CreateFeaturestoreRequest` with auto-scaling, use it with `aiplatform_v1.FeaturestoreServiceClient` to create Featurestore:"
+        "Auto scaling is available in v1beta1 since v1.11. Below is the example for the `CreateFeaturestoreRequest` with auto-scaling, use it with `aiplatform_v1beta1.FeaturestoreServiceClient` to create Featurestore:"
      ]
    },
    {
@@ -630,17 +600,17 @@
      },
      "outputs": [],
      "source": [
-        "from google.cloud.aiplatform_v1.types import \\\n",
-        "    featurestore as v1_featurestore_pb2\n",
-        "from google.cloud.aiplatform_v1.types import \\\n",
-        "    featurestore_service as v1_featurestore_service_pb2\n",
+        "from google.cloud.aiplatform_v1beta1.types import \\\n",
+        "    featurestore as v1beta1_featurestore_pb2\n",
+        "from google.cloud.aiplatform_v1beta1.types import \\\n",
+        "    featurestore_service as v1beta1_featurestore_service_pb2\n",
        "\n",
-        "create_featurestore_request = v1_featurestore_service_pb2.CreateFeaturestoreRequest(\n",
+        "create_featurestore_request = v1beta1_featurestore_service_pb2.CreateFeaturestoreRequest(\n",
        "    parent=BASE_RESOURCE_PATH,\n",
        "    featurestore_id=FEATURESTORE_ID,\n",
-        "    featurestore=v1_featurestore_pb2.Featurestore(\n",
-        "        online_serving_config=v1_featurestore_pb2.Featurestore.OnlineServingConfig(\n",
-        "            scaling=v1_featurestore_pb2.Featurestore.OnlineServingConfig.Scaling(\n",
+        "    featurestore=v1beta1_featurestore_pb2.Featurestore(\n",
+        "        online_serving_config=v1beta1_featurestore_pb2.Featurestore.OnlineServingConfig(\n",
+        "            scaling=v1beta1_featurestore_pb2.Featurestore.OnlineServingConfig.Scaling(\n",
        "                min_node_count=1, max_node_count=5\n",
        "            )\n",
        "        ),\n",
@@ -711,7 +681,7 @@
        "id": "dPkT7KDuEvWv"
      },
      "source": [
-        "Feature [monitoring](https://cloud.google.com/vertex-ai/docs/featurestore/monitoring) is in preview, so you need to use v1 Python. Import feature analysis is only available through SDK for now."
+        "Feature [monitoring](https://cloud.google.com/vertex-ai/docs/featurestore/monitoring) is in preview, so you need to use v1beta1 Python. Import feature analysis is only available through SDK for now."
      ]
    },
    {
@@ -722,35 +692,36 @@
      },
      "outputs": [],
      "source": [
-        "from google.cloud.aiplatform_v1 import \\\n",
-        "    FeaturestoreServiceClient as v1_FeaturestoreServiceClient\n",
-        "from google.cloud.aiplatform_v1.types import entity_type as v1_entity_type_pb2\n",
-        "from google.cloud.aiplatform_v1.types import \\\n",
-        "    featurestore_monitoring as v1_featurestore_monitoring_pb2\n",
-        "from google.cloud.aiplatform_v1.types import \\\n",
-        "    featurestore_service as v1_featurestore_service_pb2\n",
+        "from google.cloud.aiplatform_v1beta1 import \\\n",
+        "    FeaturestoreServiceClient as v1beta1_FeaturestoreServiceClient\n",
+        "from google.cloud.aiplatform_v1beta1.types import \\\n",
+        "    entity_type as v1beta1_entity_type_pb2\n",
+        "from google.cloud.aiplatform_v1beta1.types import \\\n",
+        "    featurestore_monitoring as v1beta1_featurestore_monitoring_pb2\n",
+        "from google.cloud.aiplatform_v1beta1.types import \\\n",
+        "    featurestore_service as v1beta1_featurestore_service_pb2\n",
        "\n",
-        "v1_admin_client = v1_FeaturestoreServiceClient(\n",
+        "v1beta1_admin_client = v1beta1_FeaturestoreServiceClient(\n",
        "    client_options={\"api_endpoint\": API_ENDPOINT}\n",
        ")\n",
        "\n",
        "# Enable import feature analysis for users entity type.\n",
        "# All Features belonging to this EntityType will by default inherit the monitoring config.\n",
-        "v1_admin_client.update_entity_type(\n",
-        "    v1_featurestore_service_pb2.UpdateEntityTypeRequest(\n",
-        "        entity_type=v1_entity_type_pb2.EntityType(\n",
+        "v1beta1_admin_client.update_entity_type(\n",
+        "    v1beta1_featurestore_service_pb2.UpdateEntityTypeRequest(\n",
+        "        entity_type=v1beta1_entity_type_pb2.EntityType(\n",
        "            name=admin_client.entity_type_path(\n",
        "                PROJECT_ID, REGION, FEATURESTORE_ID, \"users\"\n",
        "            ),\n",
-        "            monitoring_config=v1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig(\n",
-        "                import_features_analysis=v1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig.ImportFeaturesAnalysis(\n",
-        "                    anomaly_detection_baseline=v1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig.ImportFeaturesAnalysis.Baseline.LATEST_STATS,\n",
-        "                    state=v1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig.ImportFeaturesAnalysis.State.ENABLED,\n",
+        "            monitoring_config=v1beta1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig(\n",
+        "                import_features_analysis=v1beta1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig.ImportFeaturesAnalysis(\n",
+        "                    anomaly_detection_baseline=v1beta1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig.ImportFeaturesAnalysis.Baseline.LATEST_STATS,\n",
+        "                    state=v1beta1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig.ImportFeaturesAnalysis.State.ENABLED,\n",
        "                ),\n",
-        "                numerical_threshold_config=v1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig.ThresholdConfig(\n",
+        "                numerical_threshold_config=v1beta1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig.ThresholdConfig(\n",
        "                    value=0.001,\n",
        "                ),\n",
-        "                categorical_threshold_config=v1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig.ThresholdConfig(\n",
+        "                categorical_threshold_config=v1beta1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig.ThresholdConfig(\n",
        "                    value=0.001,\n",
        "                ),\n",
        "            ),\n",
@@ -765,7 +736,7 @@
        "id": "85b1f59fbf6d"
      },
      "source": [
-        "The easiest way to set up snapshot analysis for now is using [console UI](https://console.cloud.google.com/vertex-ai/features). For completeness, below is example to do this using v1 SDK.\n",
+        "The easiest way to set up snapshot analysis for now is using [console UI](https://console.cloud.google.com/vertex-ai/features). For completeness, below is example to do this using v1beta1 SDK.\n",
        "\n",
        "You can view monitoring statistics on [console UI](https://console.cloud.google.com/vertex-ai/features)."
      ]
@@ -778,35 +749,36 @@
      },
      "outputs": [],
      "source": [
-        "from google.cloud.aiplatform_v1 import \\\n",
-        "    FeaturestoreServiceClient as v1_FeaturestoreServiceClient\n",
-        "from google.cloud.aiplatform_v1.types import entity_type as v1_entity_type_pb2\n",
-        "from google.cloud.aiplatform_v1.types import \\\n",
-        "    featurestore_monitoring as v1_featurestore_monitoring_pb2\n",
-        "from google.cloud.aiplatform_v1.types import \\\n",
-        "    featurestore_service as v1_featurestore_service_pb2\n",
+        "from google.cloud.aiplatform_v1beta1 import \\\n",
+        "    FeaturestoreServiceClient as v1beta1_FeaturestoreServiceClient\n",
+        "from google.cloud.aiplatform_v1beta1.types import \\\n",
+        "    entity_type as v1beta1_entity_type_pb2\n",
+        "from google.cloud.aiplatform_v1beta1.types import \\\n",
+        "    featurestore_monitoring as v1beta1_featurestore_monitoring_pb2\n",
+        "from google.cloud.aiplatform_v1beta1.types import \\\n",
+        "    featurestore_service as v1beta1_featurestore_service_pb2\n",
        "\n",
-        "v1_admin_client = v1_FeaturestoreServiceClient(\n",
+        "v1beta1_admin_client = v1beta1_FeaturestoreServiceClient(\n",
        "    client_options={\"api_endpoint\": API_ENDPOINT}\n",
        ")\n",
        "\n",
        "# Enable snapshot analysis for users entity type.\n",
        "# All Features belonging to this EntityType will by default inherit the monitoring config.\n",
-        "v1_admin_client.update_entity_type(\n",
-        "    v1_featurestore_service_pb2.UpdateEntityTypeRequest(\n",
-        "        entity_type=v1_entity_type_pb2.EntityType(\n",
+        "v1beta1_admin_client.update_entity_type(\n",
+        "    v1beta1_featurestore_service_pb2.UpdateEntityTypeRequest(\n",
+        "        entity_type=v1beta1_entity_type_pb2.EntityType(\n",
        "            name=admin_client.entity_type_path(\n",
        "                PROJECT_ID, REGION, FEATURESTORE_ID, \"users\"\n",
        "            ),\n",
-        "            monitoring_config=v1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig(\n",
-        "                snapshot_analysis=v1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig.SnapshotAnalysis(\n",
-        "                    monitoring_interval_days=1,  # 1 day\n",
+        "            monitoring_config=v1beta1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig(\n",
+        "                snapshot_analysis=v1beta1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig.SnapshotAnalysis(\n",
+        "                    monitoring_interval=Duration(seconds=86400),  # 1 day\n",
        "                    staleness_days=30,\n",
        "                ),\n",
-        "                numerical_threshold_config=v1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig.ThresholdConfig(\n",
+        "                numerical_threshold_config=v1beta1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig.ThresholdConfig(\n",
        "                    value=0.001,\n",
        "                ),\n",
-        "                categorical_threshold_config=v1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig.ThresholdConfig(\n",
+        "                categorical_threshold_config=v1beta1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig.ThresholdConfig(\n",
        "                    value=0.001,\n",
        "                ),\n",
        "            ),\n",
@@ -919,8 +891,8 @@
      "source": [
        "## Search created features\n",
        "\n",
-        "While the [ListFeatures](https://cloud.google.com/vertex-ai/docs/reference/rpc/google.cloud.aiplatform.v1#google.cloud.aiplatform.v1.FeaturestoreService.ListFeatures) method allows you to easily view all features of a single\n",
-        "entity type, the [SearchFeatures](https://cloud.google.com/vertex-ai/docs/reference/rpc/google.cloud.aiplatform.v1#google.cloud.aiplatform.v1.FeaturestoreService.SearchFeatures) method searches across all featurestores\n",
+        "While the [ListFeatures](https://cloud.google.com/vertex-ai/docs/reference/rpc/google.cloud.aiplatform.v1beta1#google.cloud.aiplatform.v1beta1.FeaturestoreService.ListFeatures) method allows you to easily view all features of a single\n",
+        "entity type, the [SearchFeatures](https://cloud.google.com/vertex-ai/docs/reference/rpc/google.cloud.aiplatform.v1beta1#google.cloud.aiplatform.v1beta1.FeaturestoreService.SearchFeatures) method searches across all featurestores\n",
        "and entity types in a given location (such as `us-central1`). This can help you discover features that were created by someone else.\n",
        "\n",
        "You can query based on feature properties including feature ID, entity type ID,\n",
@@ -1234,7 +1206,7 @@
      },
      "source": [
        "The\n",
-        "[Online Serving APIs](https://cloud.google.com/vertex-ai/docs/reference/rpc/google.cloud.aiplatform.v1#featurestoreonlineservingservice)\n",
+        "[Online Serving APIs](https://cloud.google.com/vertex-ai/docs/reference/rpc/google.cloud.aiplatform.v1beta1#featurestoreonlineservingservice)\n",
        "lets you serve feature values for small batches of entities. It's designed for latency-sensitive service, such as online model prediction. For example, for a movie service, you might want to quickly shows movies that the current user would most likely watch by using online predictions."
      ]
    },
@@ -29,8 +29,6 @@
        "id": "JAPoU8Sm5E6e"
      },
      "source": [
-        "# Online and Batch predictions using Vertex AI Feature Store\n",
-        "\n",
        "<table align=\"left\">\n",
        "  <td>\n",
        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/feature_store/sdk-feature-store.ipynb\">\n",
@@ -53,22 +51,19 @@
    {
      "cell_type": "markdown",
      "metadata": {
-        "id": "c4aaea3bab5e"
+        "id": "tvgnzT1CKxrO"
      },
      "source": [
        "## Overview\n",
        "\n",
        "This notebook introduces Vertex AI Feature Store, a managed cloud service for machine learning engineers and data scientists to store, serve, manage and share machine learning features at a large scale.\n",
        "\n",
-        "This notebook assumes that you understand basic Google Cloud concepts such as [Project](https://cloud.google.com/storage/docs/projects), [Storage](https://cloud.google.com/storage) and [Vertex AI](https://cloud.google.com/vertex-ai/docs). Some machine learning knowledge is also helpful but not required.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "71779c8088bf"
-      },
-      "source": [
+        "This notebook assumes that you understand basic Google Cloud concepts such as [Project](https://cloud.google.com/storage/docs/projects), [Storage](https://cloud.google.com/storage) and [Vertex AI](https://cloud.google.com/vertex-ai/docs). Some machine learning knowledge is also helpful but not required.\n",
+        "\n",
+        "### Dataset\n",
+        "\n",
+        "This notebook uses a movie recommendation dataset as an example throughout all the sessions. The task is to train a model to predict if a user is going to watch a movie and serve this model online. \n",
+        "\n",
        "### Objective\n",
        "\n",
        "In this notebook, you will learn how to use `Vertex AI Feature Store` to import feature data, and to access the feature data for both online serving and offline tasks, such as training.\n",
@@ -84,26 +79,8 @@
        "- Create featurestore, entity type, and feature resources.\n",
        "- Import feature data into `Vertex AI Feature Store` resource.\n",
        "- Serve online prediction requests using the imported features.\n",
-        "- Access imported features in offline jobs, such as training jobs."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "55e01a856f57"
-      },
-      "source": [
-        "### Dataset\n",
+        "- Access imported features in offline jobs, such as training jobs.\n",
        "\n",
-        "This notebook uses a movie recommendation dataset as an example throughout all the sessions. The task is to train a model to predict if a user is going to watch a movie and serve this model online."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "tvgnzT1CKxrO"
-      },
-      "source": [
        "### Costs \n",
        "\n",
        "This tutorial uses billable components of Google Cloud:\n",
@@ -285,15 +262,7 @@
      },
      "outputs": [],
      "source": [
-        "import os\n",
-        "\n",
-        "PROJECT_ID = \"\"\n",
-        "\n",
-        "# Get your Google Cloud project ID from gcloud\n",
-        "if not os.getenv(\"IS_TESTING\"):\n",
-        "    shell_output = !gcloud config list --format 'value(core.project)' 2>/dev/null\n",
-        "    PROJECT_ID = shell_output[0]\n",
-        "    print(\"Project ID: \", PROJECT_ID)"
+        "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}"
      ]
    },
    {
@@ -306,7 +275,10 @@
      "source": [
        "if PROJECT_ID == \"\" or PROJECT_ID is None:\n",
        "    PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "print(\"Project ID: \", PROJECT_ID)"
+        "    # Get your GCP project id from gcloud\n",
+        "    shell_output = ! gcloud config list --format 'value(core.project)' 2>/dev/null\n",
+        "    PROJECT_ID = shell_output[0]\n",
+        "    print(\"Project ID:\", PROJECT_ID)"
      ]
    },
    {
@@ -348,9 +320,7 @@
      },
      "outputs": [],
      "source": [
-        "REGION = \"[your-region]\"  # @param {type:\"string\"}\n",
-        "if REGION == \"[your-region]\":\n",
-        "    REGION = \"us-central1\""
+        "REGION = \"us-central1\"  # @param {type: \"string\"}"
      ]
    },
    {
@@ -359,9 +329,9 @@
        "id": "timestamp"
      },
      "source": [
-        "#### UUID\n",
+        "#### Timestamp\n",
        "\n",
-        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
+        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append the timestamp onto the name of resources you create in this tutorial."
      ]
    },
    {
@@ -372,16 +342,9 @@
      },
      "outputs": [],
      "source": [
-        "import random\n",
-        "import string\n",
+        "from datetime import datetime\n",
        "\n",
-        "\n",
-        "# Generate a uuid of a specifed length(default=8)\n",
-        "def generate_uuid(length: int = 8) -> str:\n",
-        "    return \"\".join(random.choices(string.ascii_lowercase + string.digits, k=length))\n",
-        "\n",
-        "\n",
-        "UUID = generate_uuid()"
+        "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
      ]
    },
    {
@@ -478,7 +441,7 @@
      "source": [
        "from google.cloud.aiplatform import Feature, Featurestore\n",
        "\n",
-        "FEATURESTORE_ID = \"movie_prediction\" + UUID\n",
+        "FEATURESTORE_ID = \"movie_prediction\"\n",
        "INPUT_CSV_FILE = \"gs://cloud-samples-data-us-central1/vertex-ai/feature-store/datasets/movie_prediction.csv\"\n",
        "ONLINE_STORE_FIXED_NODE_COUNT = 1"
      ]
@@ -55,7 +55,7 @@
      "source": [
        "### Dataset\n",
        "\n",
-        "The dataset used for this tutorial is the Bank Marketing. This dataset does not require any feature engineering. The version of the dataset you will use in this tutorial is stored in a public Cloud Storage bucket."
+        "The dataset used for this tutorial is the [Bank Marketing](https://pantheon.corp.google.com/storage/browser/_details/cloud-ml-tables-data/bank-marketing.csv) . This dataset does not require any feature engineering. The version of the dataset you will use in this tutorial is stored in a public Cloud Storage bucket."
      ]
    },
    {
@@ -32,26 +32,18 @@
        "# Vertex AI: Vertex AI Migration: AutoML Image Object Detection\n",
        "\n",
        "<table align=\"left\">\n",
-        "\n",
        "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ5 Vertex SDK AutoML Image Object Detection.ipynb\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/ai-platform-samples/blob/master/vertex-ai-samples/tree/master/notebooks/official/migration/UJ5%20Vertex%20SDK%20AutoML%20Image%20Object%20Detection.ipynb\">\n",
        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ5 Vertex SDK AutoML Image Object Detection.ipynb\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/ai-platform-samples/blob/master/vertex-ai-samples/tree/master/notebooks/official/migration/UJ5%20Vertex%20SDK%20AutoML%20Image%20Object%20Detection.ipynb\">\n",
        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
        "      View on GitHub\n",
        "    </a>\n",
        "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ5 Vertex SDK AutoML Image Object Detection.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
-        "    </a>\n",
-        "  </td>                                                                                               \n",
        "</table>\n",
-        "\n",
        "<br/><br/><br/>"
      ]
    },
@@ -127,7 +119,7 @@
      "source": [
        "## Installation\n",
        "\n",
-        "Install the latest version of Vertex AI SDK for Python."
+        "Install the latest version of Vertex SDK for Python."
      ]
    },
    {
@@ -146,7 +138,7 @@
        "else:\n",
        "    USER_FLAG = \"\"\n",
        "\n",
-        "! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG -q"
+        "! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG"
      ]
    },
    {
@@ -158,6 +150,17 @@
        "Install the latest GA version of *google-cloud-storage* library as well."
      ]
    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "install_storage"
+      },
+      "outputs": [],
+      "source": [
+        "! pip3 install -U google-cloud-storage $USER_FLAG"
+      ]
+    },
    {
      "cell_type": "code",
      "execution_count": null,
@@ -166,9 +169,8 @@
      },
      "outputs": [],
      "source": [
-        "! pip3 install -U google-cloud-storage $USER_FLAG -q\n",
-        "\n",
-        "! pip3 install --upgrade tensorflow $USER_FLAG -q"
+        "if os.getenv(\"IS_TESTING\"):\n",
+        "    ! pip3 install --upgrade tensorflow $USER_FLAG"
      ]
    },
    {
@@ -295,10 +297,7 @@
      },
      "outputs": [],
      "source": [
-        "REGION = \"[your-region]\"  # @param {type: \"string\"}\n",
-        "\n",
-        "if REGION == \"[your-region]\":\n",
-        "    REGION = \"us-central1\""
+        "REGION = \"us-central1\"  # @param {type: \"string\"}"
      ]
    },
    {
@@ -307,9 +306,9 @@
        "id": "timestamp"
      },
      "source": [
-        "#### UUID\n",
+        "#### Timestamp\n",
        "\n",
-        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
+        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append the timestamp onto the name of resources you create in this tutorial."
      ]
    },
    {
@@ -320,16 +319,9 @@
      },
      "outputs": [],
      "source": [
-        "import random\n",
-        "import string\n",
+        "from datetime import datetime\n",
        "\n",
-        "\n",
-        "# Generate a uuid of a specifed length(default=8)\n",
-        "def generate_uuid(length: int = 8) -> str:\n",
-        "    return \"\".join(random.choices(string.ascii_lowercase + string.digits, k=length))\n",
-        "\n",
-        "\n",
-        "UUID = generate_uuid()"
+        "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
      ]
    },
    {
@@ -340,7 +332,7 @@
      "source": [
        "### Authenticate your Google Cloud account\n",
        "\n",
-        "**If you are using Vertex AI Workbench Notebooks**, your environment is already authenticated. Skip this step.\n",
+        "**If you are using Google Cloud Notebooks**, your environment is already authenticated. Skip this step.\n",
        "\n",
        "**If you are using Colab**, run the cell below and follow the instructions when prompted to authenticate your account via oAuth.\n",
        "\n",
@@ -375,11 +367,8 @@
        "import os\n",
        "import sys\n",
        "\n",
-        "# If on Vertex AI Workbench, then don't execute this code\n",
-        "IS_COLAB = \"google.colab\" in sys.modules\n",
-        "if not os.path.exists(\"/opt/deeplearning/metadata/env_version\") and not os.getenv(\n",
-        "    \"DL_ANACONDA_HOME\"\n",
-        "):\n",
+        "# If on Google Cloud Notebook, then don't execute this code\n",
+        "if not os.path.exists(\"/opt/deeplearning/metadata/env_version\"):\n",
        "    if \"google.colab\" in sys.modules:\n",
        "        from google.colab import auth as google_auth\n",
        "\n",
@@ -415,8 +404,7 @@
      },
      "outputs": [],
      "source": [
-        "BUCKET_NAME = \"[your-bucket-name]\"  # @param {type:\"string\"}\n",
-        "BUCKET_URI = f\"gs://{BUCKET_NAME}\""
+        "BUCKET_NAME = \"gs://[your-bucket-name]\"  # @param {type:\"string\"}"
      ]
    },
    {
@@ -427,9 +415,8 @@
      },
      "outputs": [],
      "source": [
-        "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"[your-bucket-name]\":\n",
-        "    BUCKET_NAME = PROJECT_ID + \"aip-\" + UUID\n",
-        "    BUCKET_URI = f\"gs://{BUCKET_NAME}\""
+        "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"gs://[your-bucket-name]\":\n",
+        "    BUCKET_NAME = \"gs://\" + PROJECT_ID + \"aip-\" + TIMESTAMP"
      ]
    },
    {
@@ -449,7 +436,7 @@
      },
      "outputs": [],
      "source": [
-        "! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_URI"
+        "! gsutil mb -l $REGION $BUCKET_NAME"
      ]
    },
    {
@@ -469,7 +456,7 @@
      },
      "outputs": [],
      "source": [
-        "! gsutil ls -al $BUCKET_URI"
+        "! gsutil ls -al $BUCKET_NAME"
      ]
    },
    {
@@ -514,7 +501,7 @@
      },
      "outputs": [],
      "source": [
-        "aip.init(project=PROJECT_ID, staging_bucket=BUCKET_URI)"
+        "aip.init(project=PROJECT_ID, staging_bucket=BUCKET_NAME)"
      ]
    },
    {
@@ -616,7 +603,7 @@
      "outputs": [],
      "source": [
        "dataset = aip.ImageDataset.create(\n",
-        "    display_name=\"Salads\" + \"_\" + UUID,\n",
+        "    display_name=\"Salads\" + \"_\" + TIMESTAMP,\n",
        "    gcs_source=[IMPORT_FILE],\n",
        "    import_schema_uri=aip.schema.dataset.ioformat.image.bounding_box,\n",
        ")\n",
@@ -701,7 +688,7 @@
      "outputs": [],
      "source": [
        "dag = aip.AutoMLImageTrainingJob(\n",
-        "    display_name=\"salads_\" + UUID,\n",
+        "    display_name=\"salads_\" + TIMESTAMP,\n",
        "    prediction_type=\"object_detection\",\n",
        "    multi_label=False,\n",
        "    model_type=\"CLOUD\",\n",
@@ -755,7 +742,7 @@
      "source": [
        "model = dag.run(\n",
        "    dataset=dataset,\n",
-        "    model_display_name=\"salads_\" + UUID,\n",
+        "    model_display_name=\"salads_\" + TIMESTAMP,\n",
        "    training_fraction_split=0.8,\n",
        "    validation_fraction_split=0.1,\n",
        "    test_fraction_split=0.1,\n",
@@ -828,7 +815,7 @@
      "outputs": [],
      "source": [
        "# Get model resource ID\n",
-        "models = aip.Model.list(filter=\"display_name=salads_\" + UUID)\n",
+        "models = aip.Model.list(filter=\"display_name=salads_\" + TIMESTAMP)\n",
        "\n",
        "# Get a reference to the Model Service client\n",
        "client_options = {\"api_endpoint\": f\"{REGION}-aiplatform.googleapis.com\"}\n",
@@ -958,11 +945,11 @@
        "file_1 = test_item_1.split(\"/\")[-1]\n",
        "file_2 = test_item_2.split(\"/\")[-1]\n",
        "\n",
-        "! gsutil cp $test_item_1 $BUCKET_URI/$file_1\n",
-        "! gsutil cp $test_item_2 $BUCKET_URI/$file_2\n",
+        "! gsutil cp $test_item_1 $BUCKET_NAME/$file_1\n",
+        "! gsutil cp $test_item_2 $BUCKET_NAME/$file_2\n",
        "\n",
-        "test_item_1 = BUCKET_URI + \"/\" + file_1\n",
-        "test_item_2 = BUCKET_URI + \"/\" + file_2"
+        "test_item_1 = BUCKET_NAME + \"/\" + file_1\n",
+        "test_item_2 = BUCKET_NAME + \"/\" + file_2"
      ]
    },
    {
@@ -995,7 +982,7 @@
        "\n",
        "import tensorflow as tf\n",
        "\n",
-        "gcs_input_uri = BUCKET_URI + \"/test.jsonl\"\n",
+        "gcs_input_uri = BUCKET_NAME + \"/test.jsonl\"\n",
        "with tf.io.gfile.GFile(gcs_input_uri, \"w\") as f:\n",
        "    data = {\"content\": test_item_1, \"mime_type\": \"image/jpeg\"}\n",
        "    f.write(json.dumps(data) + \"\\n\")\n",
@@ -1031,9 +1018,9 @@
      "outputs": [],
      "source": [
        "batch_predict_job = model.batch_predict(\n",
-        "    job_display_name=\"salads_\" + UUID,\n",
+        "    job_display_name=\"salads_\" + TIMESTAMP,\n",
        "    gcs_source=gcs_input_uri,\n",
-        "    gcs_destination_prefix=BUCKET_URI,\n",
+        "    gcs_destination_prefix=BUCKET_NAME,\n",
        "    sync=False,\n",
        ")\n",
        "\n",
@@ -1391,25 +1378,60 @@
      },
      "outputs": [],
      "source": [
-        "# Delete the dataset using the Vertex dataset object\n",
+        "delete_all = True\n",
        "\n",
-        "dataset.delete()\n",
+        "if delete_all:\n",
+        "    # Delete the dataset using the Vertex dataset object\n",
+        "    try:\n",
+        "        if \"dataset\" in globals():\n",
+        "            dataset.delete()\n",
+        "    except Exception as e:\n",
+        "        print(e)\n",
        "\n",
-        "# Delete the model using the Vertex model object\n",
-        "model.delete()\n",
+        "    # Delete the model using the Vertex model object\n",
+        "    try:\n",
+        "        if \"model\" in globals():\n",
+        "            model.delete()\n",
+        "    except Exception as e:\n",
+        "        print(e)\n",
        "\n",
-        "# Delete the endpoint using the Vertex endpoint object\n",
-        "endpoint.delete()\n",
+        "    # Delete the endpoint using the Vertex endpoint object\n",
+        "    try:\n",
+        "        if \"endpoint\" in globals():\n",
+        "            endpoint.delete()\n",
+        "    except Exception as e:\n",
+        "        print(e)\n",
        "\n",
-        "# Delete the AutoML or Pipeline trainig job\n",
+        "    # Delete the AutoML or Pipeline trainig job\n",
+        "    try:\n",
+        "        if \"dag\" in globals():\n",
+        "            dag.delete()\n",
+        "    except Exception as e:\n",
+        "        print(e)\n",
        "\n",
-        "dag.delete()\n",
+        "    # Delete the custom trainig job\n",
+        "    try:\n",
+        "        if \"job\" in globals():\n",
+        "            job.delete()\n",
+        "    except Exception as e:\n",
+        "        print(e)\n",
        "\n",
-        "# Delete the batch prediction job using the Vertex batch prediction object\n",
-        "batch_predict_job.delete()\n",
+        "    # Delete the batch prediction job using the Vertex batch prediction object\n",
+        "    try:\n",
+        "        if \"batch_predict_job\" in globals():\n",
+        "            batch_predict_job.delete()\n",
+        "    except Exception as e:\n",
+        "        print(e)\n",
        "\n",
-        "if \"BUCKET_NAME\" in globals():\n",
-        "    ! gsutil rm -r $BUCKET_URI"
+        "    # Delete the hyperparameter tuning job using the Vertex hyperparameter tuning object\n",
+        "    try:\n",
+        "        if \"hpt_job\" in globals():\n",
+        "            hpt_job.delete()\n",
+        "    except Exception as e:\n",
+        "        print(e)\n",
+        "\n",
+        "    if \"BUCKET_NAME\" in globals():\n",
+        "        ! gsutil rm -r $BUCKET_NAME"
      ]
    }
  ],
@@ -29,7 +29,7 @@
        "id": "JAPoU8Sm5E6e"
      },
      "source": [
-        "# Vertex AI Pipelines: Loan eligibility prediction using `google-cloud-pipeline-components` and Spark ML\n",
+        "# Vertex AI Pipelines: Loan eligibility prediction using google-cloud-pipeline-components and Spark ML\n",
        "\n",
        "<table align=\"left\">\n",
        "\n",
@@ -206,7 +206,7 @@
        "    \n",
        "!pip3 install {USER_FLAG} --upgrade google-cloud-aiplatform==1.11.0 \\\n",
        "                                    kfp==1.8.11 \\\n",
-        "                                    google-cloud-pipeline-components==1.0.18 --quiet --no-warn-conflicts"
+        "                                    google-cloud-pipeline-components==1.0.1 --quiet --no-warn-conflicts"
      ]
    },
    {
@@ -733,7 +733,9 @@
        "from pathlib import Path as path\n",
        "from typing import NamedTuple\n",
        "\n",
+        "# Part 1 - ML Training\n",
        "from google.cloud import aiplatform as vertex_ai\n",
+        "from google_cloud_pipeline_components import aiplatform as vertex_ai_components\n",
        "from kfp.v2 import compiler, dsl\n",
        "from kfp.v2.dsl import (Artifact, ClassificationMetrics, Condition, Input,\n",
        "                        Metrics, Output, component)"
@@ -761,14 +763,14 @@
        "PIPELINE_ROOT = f\"{BUCKET_URI}/pipelines\"\n",
        "PIPELINE_PACKAGE_PATH = str(BUILD_PATH / f\"pipeline_{UUID}.json\")\n",
        "RUNTIME_CONTAINER_IMAGE = f\"gcr.io/{PROJECT_ID}/{RUNTIME_IMAGE}:{IMAGE_TAG}\"\n",
-        "ML_APPLICATION = \"loan-eligibility\"\n",
-        "TASK = \"sparkml\"\n",
+        "ML_APPLICATION = \"spark\"\n",
+        "TASK = \"classifier\"\n",
        "MODEL_TYPE = \"rfor\"\n",
        "VERSION = \"1.0.0\"\n",
        "MODEL_NAME = f\"{ML_APPLICATION}-{TASK}-{MODEL_TYPE}-{VERSION}\"\n",
-        "ARTIFACT_URI = f\"{BUCKET_URI}/deliverables/bundle/{UUID}\"\n",
        "\n",
        "# Preprocessing\n",
+        "PREPROCESSING_BATCH_ID = f\"data-preprocessing-{UUID}\"\n",
        "PREPROCESSING_PYTHON_FILE_URI = f\"{BUCKET_URI}/src/data_preprocessing.py\"\n",
        "PROCESSED_DATA_URI = f\"{BUCKET_URI}/data/processed\"\n",
        "PREPROCESSING_ARGS = [\n",
@@ -783,6 +785,7 @@
        "GCS_PREPROCESSED_URI = f\"{PROCESSED_DATA_URI}/*/?.csv\"\n",
        "\n",
        "# Training\n",
+        "TRAINING_BATCH_ID = f\"model-training-{UUID}\"\n",
        "TRAINING_PYTHON_FILE_URI = f\"{BUCKET_URI}/src/model_training.py\"\n",
        "MODEL_URI = f\"{BUCKET_URI}/deliverables/model/rfor/{UUID}/train_model\"\n",
        "METRICS_URI = f\"{BUCKET_URI}/deliverables/metrics/rfor/{UUID}/train_metrics.json\"\n",
@@ -797,9 +800,10 @@
        "\n",
        "# Condition\n",
        "AUPR_THRESHOLD = 0.5\n",
-        "AUPR_HYPERTUNE_CONDITION = \"hypertune\"\n",
+        "AUPR_HYPERTUNE_CONDITION = \"[AUPR_HYPERTUNE]\"\n",
        "\n",
        "# Hypertuning\n",
+        "HPT_TRAINING_BATCH_ID = f\"hyper-tuning-{UUID}\"\n",
        "HPT_PYTHON_FILE_URI = f\"{BUCKET_URI}/src/hp_tuning.py\"\n",
        "HPT_MODEL_URI = f\"{BUCKET_URI}/deliverables/model/rfor/{UUID}/model\"\n",
        "HPT_METRICS_URI = f\"{BUCKET_URI}/deliverables/metrics/rfor/{UUID}/metrics.json\"\n",
@@ -810,24 +814,7 @@
        "    HPT_MODEL_URI,\n",
        "    \"--metrics-path\",\n",
        "    HPT_METRICS_URI,\n",
-        "]\n",
-        "HPT_BUNDLE_URI = f\"{ARTIFACT_URI}/model.zip\"\n",
-        "HPT_ARGS = [\n",
-        "    \"--train-path\",\n",
-        "    PROCESSED_DATA_URI,\n",
-        "    \"--model-path\",\n",
-        "    HPT_MODEL_URI,\n",
-        "    \"--metrics-path\",\n",
-        "    HPT_METRICS_URI,\n",
-        "    \"--bundle-path\",\n",
-        "    HPT_BUNDLE_URI,\n",
-        "]\n",
-        "HPT_RUNTIME_PROPERTIES = {\n",
-        "    \"spark.jars.packages\": \"ml.combust.mleap:mleap-spark-base_2.12:0.20.0,ml.combust.mleap:mleap-spark_2.12:0.20.0\"\n",
-        "}\n",
-        "\n",
-        "# Deploy\n",
-        "SERVING_IMAGE_URI = f\"{REGION}-docker.pkg.dev/{PROJECT_ID}/{REPO_NAME}/spark-ml-serving\""
+        "]"
      ]
    },
    {
@@ -856,7 +843,7 @@
        "id": "LB2aM7VyRyZG"
      },
      "source": [
-        "## Build the Vertex Pipeline to train and deploy a Spark model\n",
+        "## PART I - Build the Vertex Pipeline to train and deploy a Spark model\n",
        "\n",
        "In this case, the ML pipeline includes the following steps:\n",
        "\n",
@@ -864,16 +851,10 @@
        "2.   Train an `RandomForestClassifier` with `DataprocPySparkBatchOp`\n",
        "3.   Run a custom component in order to evaluate the model\n",
        "\n",
-        "If the model respects the performance condition, then:\n",
+        "If the model respects the performance condition, then\n",
        "\n",
        "4.   Hypertune the `RandomForestClassifier` with `DataprocPySparkBatchOp`\n",
-        "5.   Serializes the model to MLeap format to use the model outside of Spark.\n",
-        "\n",
-        "If the `deploy_model` pipeline parameter is set to `True`:\n",
-        "\n",
-        "6.   Upload the model to Vertex AI Model Registry.\n",
-        "7.   Creates a Vertex AI endpoint.\n",
-        "8.   Deploys the model to the Vertex AI endpoint for serving online prediction requests.\n"
+        "5.   Register the model in the Vertex AI Model Registry\n"
      ]
    },
    {
@@ -1449,9 +1430,7 @@
        "\n",
        "- `--train-path`: The GCS path of the training sample.\n",
        "- `--model-path`: The GCS path to store the trained model.\n",
-        "- `--metrics-path`: The GCS path to store the metrics of model.\n",
-        "\n",
-        "The hyperparameter tuning job will also serialize the best performing model to an MLeap bundle, which can be imported to Vertex AI as a model for serving predictions - see the *Serve your model in Vertex AI* section further below."
+        "- `--metrics-path`: The GCS path to store the metrics of model."
      ]
    },
    {
@@ -1488,9 +1467,6 @@
        "except ImportError as e:\n",
        "    print('WARN: Something wrong with pyspark library. Please check configuration settings!')\n",
        "    print(e)\n",
-        "    \n",
-        "import mleap.pyspark\n",
-        "from mleap.pyspark.spark_support import SimpleSparkSerializer\n",
        "\n",
        "from pyspark.sql.types import StructType, DoubleType, StringType\n",
        "from pyspark.sql.functions import col, udf\n",
@@ -1596,16 +1572,6 @@
        "        ''',\n",
        "        type=str,\n",
        "        required=True)\n",
-        "    args_parser.add_argument(\n",
-        "        '--bundle-path',\n",
-        "        help='''\n",
-        "        The GCS path to store the exported MLeap bundle. \n",
-        "        Format: \n",
-        "        - locally: /path/to/dir\n",
-        "        - cloud: gs://bucket/path\n",
-        "        ''',\n",
-        "        type=str,\n",
-        "        required=True)\n",
        "    return args_parser.parse_args()\n",
        "\n",
        "\n",
@@ -1762,7 +1728,6 @@
        "    train_path = args.train_path\n",
        "    model_path = args.model_path\n",
        "    metrics_path = args.metrics_path\n",
-        "    bundle_path = args.bundle_path\n",
        "\n",
        "    try:\n",
        "        logger.info('initializing pipeline training.')\n",
@@ -1794,20 +1759,10 @@
        "        logger.info(f'load model pipeline in {model_path}.')\n",
        "        pipeline_model.write().overwrite().save(model_path)\n",
        "\n",
-        "        logger.info(f'upload metrics under {metrics_path}.')\n",
+        "        logger.info(f'Upload metrics under {metrics_path}.')\n",
        "        bucket = urlparse(model_path).netloc\n",
        "        metrics_file_path = urlparse(metrics_path).path.strip('/')\n",
        "        write_metrics(bucket, metrics, metrics_file_path)\n",
-        "        \n",
-        "        logger.info('export MLeap bundle to temporary location')\n",
-        "        pipeline_model.bestModel.serializeToBundle(f'jar:file:/tmp/bundle.zip', predictions)\n",
-        "        \n",
-        "        logger.info(f'upload MLeap bundle to {bundle_path}')\n",
-        "        bundle_file_path = urlparse(bundle_path).path.strip('/')\n",
-        "        bucket = urlparse(bundle_path).netloc\n",
-        "        logger.info(f'Copying /tmp/bundle.zip to bucket {bucket} using object name {bundle_file_path} ...')\n",
-        "        upload_file(bucket, '/tmp/bundle.zip', bundle_file_path)\n",
-        "        \n",
        "    except RuntimeError as main_error:\n",
        "        logger.error(main_error)\n",
        "    else:\n",
@@ -1852,11 +1807,11 @@
        "id": "68nYBB5GS9TB"
      },
      "source": [
-        "### Build a custom Dataproc Serverless container image\n",
+        "### Build a custom dataproc serverless image\n",
        "\n",
-        "Dataproc Serverless provides [default runtime images](https://cloud.google.com/dataproc-serverless/docs/concepts/versions/spark-runtime-versions). You can also use custom container images for your Dataproc Serverless workloads. \n",
+        "The `DataprocPySparkBatchOp` allows you to pass custom image that you use when the [provided Dataproc Serverless runtime versions](https://cloud.google.com/dataproc-serverless/docs/concepts/versions/spark-runtime-versions) does not respect your requirements. \n",
        "\n",
-        "The steps in this section builds a custom container image that includes additional dependencies. The custom container image can be specified when using the `DataprocPySparkBatchOp` component to launch the workload within a pipeline."
+        "**Note:** This step is optional and is included here for general awareness."
      ]
    },
    {
@@ -1865,7 +1820,7 @@
        "id": "GF9_5IGYqLAX"
      },
      "source": [
-        "#### Define the Dataproc Serverless custom runtime image"
+        "#### Define the Dataproc serverless custom runtime image"
      ]
    },
    {
@@ -1936,8 +1891,7 @@
        "      python \\\n",
        "      scikit-image \\\n",
        "      scikit-learn \\\n",
-        "      scipy \\\n",
-        "      mleap\n",
+        "      scipy  \n",
        "\n",
        "# (Required) Create the 'spark' group/user.\n",
        "# The GID and UID must be 1099. Home directory is required.\n",
@@ -1973,9 +1927,7 @@
        "id": "ZXzI2xInqb3V"
      },
      "source": [
-        "#### Build the Dataproc Serverless custom runtime using Google Cloud Build\n",
-        "\n",
-        "**Note:** this step may take approximately 5 to 10 minutes to complete."
+        "#### Build the Dataproc serverless custom runtime using Google Cloud Build"
      ]
    },
    {
@@ -2152,12 +2104,12 @@
    {
      "cell_type": "markdown",
      "metadata": {
-        "id": "28f3d22dd97f"
+        "id": "1-Ccx4uLDz4N"
      },
      "source": [
-        "#### Create component for passing args to hyperparameter tuning component\n",
+        "#### Model registration custom component\n",
        "\n",
-        "The following component passes the args `--train-path`, `--model-path` and `--metrics-path`, and `--bundle-path` in the required format for the hyperparamter tuning function defined earlier."
+        "Define a component to create a model resource for the trained model on Vertex AI Model registry."
      ]
    },
    {
@@ -2168,230 +2120,22 @@
      },
      "outputs": [],
      "source": [
+        "# TODO: Build a custom compiler using Spark docker image to compile the Mleap bundle\n",
+        "\n",
+        "\n",
        "@component(base_image=\"python:3.8-slim\")\n",
-        "def build_hpt_args(\n",
-        "    dataset_uri: Input[Artifact],\n",
-        "    train_path: str,\n",
-        "    model_path: str,\n",
-        "    metrics_path: str,\n",
-        "    bundle_path: str,\n",
-        ") -> list:\n",
-        "    return [\n",
-        "        \"--train-path\",\n",
-        "        train_path,\n",
-        "        \"--model-path\",\n",
-        "        model_path,\n",
-        "        \"--metrics-path\",\n",
-        "        metrics_path,\n",
-        "        \"--bundle-path\",\n",
-        "        bundle_path,\n",
-        "    ]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0db73fff95b3"
-      },
-      "source": [
-        "### (Optional) Serve your model using Vertex AI\n",
+        "def register_model(\n",
+        "    artifact_uri: str,\n",
+        "    model: Output[Artifact],\n",
+        ") -> NamedTuple(\"Outputs\", [(\"uri\", str)]):\n",
        "\n",
-        "The hyperparameter tuning task exports the best performing model as an MLeap bundle. The MLeap bundle can be imported into the Vertex AI Model Registry and used for prediction serving. See [Serving Spark ML model using Vertex AI](https://cloud.google.com/architecture/spark-ml-model-with-vertexai) for more information.\n",
-        "\n",
-        "Enable import of the MLeap bundle into the Vertex AI Model Registry and online prediction serving."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "2d7e7c8fc21b"
-      },
-      "outputs": [],
-      "source": [
-        "# Set DEPLOY_MODEL to True\n",
-        "DEPLOY_MODEL = False"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0bb792622d9f"
-      },
-      "source": [
-        "### Build the model serving container image\n",
-        "\n",
-        "A *serving container image* is required to import your model into the Model Registry. The serving container image provides the model serving implementation for the model. The following replicates the instructions from [Serving Spark ML model using Vertex AI](https://cloud.google.com/architecture/spark-ml-model-with-vertexai) to build the serving container image.\n",
-        "\n",
-        "**Note:** this step may take approximately 5 to 10 minutes to complete."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "4703a0f969a3"
-      },
-      "outputs": [],
-      "source": [
-        "DEPLOY_MODEL_CONDITION = 'deploy'\n",
-        "\n",
-        "if DEPLOY_MODEL:\n",
-        "\n",
-        "    import os\n",
-        "    \n",
-        "    CWD = os.getcwd()\n",
-        "\n",
-        "    # Clone and build the scala-sbt cloud builder\n",
-        "    ! git clone https://github.com/GoogleCloudPlatform/cloud-builders-community.git\n",
-        "    ! cd ${CWD}/cloud-builders-community/scala-sbt && \\\n",
-        "        gcloud builds submit .\n",
-        "\n",
-        "    # Clone and build the serving container code\n",
-        "    ! cd {CWD} && git clone https://github.com/GoogleCloudPlatform/vertex-ai-spark-ml-serving.git\n",
-        "    ! cd {CWD}/vertex-ai-spark-ml-serving && \\\n",
-        "        gcloud builds submit --config=cloudbuild.yaml \\\n",
-        "            --substitutions=\"_LOCATION={REGION},_REPOSITORY={REPO_NAME},_IMAGE=spark-ml-serving\" ."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "947b51adc087"
-      },
-      "source": [
-        "### Create component for importing a model artifact into a pipeline\n",
-        "\n",
-        "The pipeline uses the `ModelImportOp` component to import (upload) a model to Vertex AI Model Registry.\n",
-        "\n",
-        "The `import_model_artifact` python component creates a model artifact that can be passed to the `ModelImportOp` component."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "2ed96e7ad046"
-      },
-      "outputs": [],
-      "source": [
-        "@dsl.component(\n",
-        "    base_image=\"python:3.8-slim\",\n",
-        "    packages_to_install=[\"google-cloud-aiplatform\"],\n",
-        ")\n",
-        "def import_model_artifact(\n",
-        "    model: dsl.Output[dsl.Artifact], artifact_uri: str, serving_image_uri: str\n",
-        "):\n",
-        "    model.metadata[\"containerSpec\"] = {\n",
-        "        \"imageUri\": serving_image_uri,\n",
-        "        \"healthRoute\": \"/health\",\n",
-        "        \"predictRoute\": \"/predict\",\n",
-        "    }\n",
-        "    model.uri = artifact_uri"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0d83d9e80923"
-      },
-      "source": [
-        "The serving container requires the model schema in JSON format, which is read during container startup. See [Provide the model schema](https://cloud.google.com/architecture/spark-ml-model-with-vertexai#provide_the_model_schema) for more information.\n",
-        "\n",
-        "Write the model schema file:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "521d2f4d7992"
-      },
-      "outputs": [],
-      "source": [
-        "%%writefile $SRC/schema.json\n",
-        "{\n",
-        "  \"input\": [\n",
-        "    {\n",
-        "      \"name\": \"loan_amount\",\n",
-        "      \"type\": \"DOUBLE\"\n",
-        "    },\n",
-        "    {\n",
-        "      \"name\": \"loan_term\",\n",
-        "      \"type\": \"STRING\"\n",
-        "    },\n",
-        "    {\n",
-        "      \"name\": \"property_area\",\n",
-        "      \"type\": \"STRING\"\n",
-        "    },\n",
-        "    {\n",
-        "      \"name\": \"feature_7\",\n",
-        "      \"type\": \"DOUBLE\"\n",
-        "    },\n",
-        "    {\n",
-        "      \"name\": \"feature_3\",\n",
-        "      \"type\": \"DOUBLE\"\n",
-        "    },\n",
-        "    {\n",
-        "      \"name\": \"feature_1\",\n",
-        "      \"type\": \"DOUBLE\"\n",
-        "    },\n",
-        "    {\n",
-        "      \"name\": \"feature_9\",\n",
-        "      \"type\": \"DOUBLE\"\n",
-        "    },\n",
-        "    {\n",
-        "      \"name\": \"feature_5\",\n",
-        "      \"type\": \"DOUBLE\"\n",
-        "    },\n",
-        "    {\n",
-        "      \"name\": \"feature_0\",\n",
-        "      \"type\": \"DOUBLE\"\n",
-        "    },\n",
-        "    {\n",
-        "      \"name\": \"feature_8\",\n",
-        "      \"type\": \"DOUBLE\"\n",
-        "    },\n",
-        "    {\n",
-        "      \"name\": \"feature_4\",\n",
-        "      \"type\": \"DOUBLE\"\n",
-        "    },\n",
-        "    {\n",
-        "      \"name\": \"feature_2\",\n",
-        "      \"type\": \"DOUBLE\"\n",
-        "    },\n",
-        "    {\n",
-        "      \"name\": \"feature_6\",\n",
-        "      \"type\": \"DOUBLE\"\n",
-        "    }\n",
-        "  ],\n",
-        "  \"output\": [\n",
-        "    {\n",
-        "      \"name\": \"prediction\",\n",
-        "      \"type\": \"DOUBLE\"\n",
-        "    }\n",
-        "  ]\n",
-        "}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "d0b88e26570a"
-      },
-      "source": [
-        "Copy the model schema configuration file to GCS. The serving container reads the model schema file location from the `AIP_STORAGE_URI` environment at startup. See [Import the model into Vertex AI](https://cloud.google.com/architecture/spark-ml-model-with-vertexai#import-the-model-into-vertex-ai) for more information."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "b7763bb558f3"
-      },
-      "outputs": [],
-      "source": [
-        "! gsutil cp $SRC/schema.json $ARTIFACT_URI/schema.json"
+        "    component_outputs = NamedTuple(\n",
+        "        \"Outputs\",\n",
+        "        [\n",
+        "            (\"uri\", str),\n",
+        "        ],\n",
+        "    )\n",
+        "    return component_outputs(artifact_uri)"
      ]
    },
    {
@@ -2415,35 +2159,30 @@
      "source": [
        "@dsl.pipeline(name=PIPELINE_NAME, description=\"A pipeline to train a PySpark model.\")\n",
        "def pipeline(\n",
+        "    preprocessing_batch_id: str = PREPROCESSING_BATCH_ID,\n",
        "    preprocessing_main_python_file_uri: str = PREPROCESSING_PYTHON_FILE_URI,\n",
        "    train_data_path: str = FEATURES_TRAIN_URI,\n",
        "    preprocessed_data_path: str = PROCESSED_DATA_URI,\n",
        "    dataset_name: str = DATASET_NAME,\n",
        "    dataset_uri: str = GCS_PREPROCESSED_URI,\n",
+        "    training_batch_id: str = TRAINING_BATCH_ID,\n",
        "    training_main_python_file_uri: str = TRAINING_PYTHON_FILE_URI,\n",
        "    train_path: str = PROCESSED_DATA_URI,\n",
        "    model_path: str = MODEL_URI,\n",
        "    metrics_path: str = METRICS_URI,\n",
        "    threshold: float = AUPR_THRESHOLD,\n",
+        "    hpt_batch_id: str = HPT_TRAINING_BATCH_ID,\n",
        "    hpt_main_python_file_uri: str = HPT_PYTHON_FILE_URI,\n",
        "    hpt_model_path: str = HPT_MODEL_URI,\n",
        "    hpt_metrics_path: str = HPT_METRICS_URI,\n",
-        "    hpt_bundle_path: str = HPT_BUNDLE_URI,\n",
        "    custom_container_image: str = RUNTIME_CONTAINER_IMAGE,\n",
        "    model_name: str = MODEL_NAME,\n",
        "    project_id: str = PROJECT_ID,\n",
        "    location: str = REGION,\n",
-        "    deploy_model: bool = DEPLOY_MODEL,\n",
-        "    artifact_uri: str = ARTIFACT_URI,\n",
-        "    serving_image_uri: str = SERVING_IMAGE_URI,\n",
        "):\n",
-        "    from google_cloud_pipeline_components.v1.dataproc import \\\n",
+        "\n",
+        "    from google_cloud_pipeline_components.experimental.dataproc import \\\n",
        "        DataprocPySparkBatchOp\n",
-        "    from google_cloud_pipeline_components.v1.dataset import \\\n",
-        "        TabularDatasetCreateOp\n",
-        "    from google_cloud_pipeline_components.v1.endpoint import (EndpointCreateOp,\n",
-        "                                                              ModelDeployOp)\n",
-        "    from google_cloud_pipeline_components.v1.model import ModelUploadOp\n",
        "\n",
        "    # build preprocessed data args\n",
        "    build_preprocessing_args_op = build_preprocessing_args(\n",
@@ -2455,12 +2194,13 @@
        "        project=project_id,\n",
        "        location=location,\n",
        "        container_image=custom_container_image,\n",
+        "        batch_id=preprocessing_batch_id,\n",
        "        main_python_file_uri=preprocessing_main_python_file_uri,\n",
        "        args=build_preprocessing_args_op.output,\n",
        "    ).after(build_preprocessing_args_op)\n",
        "\n",
        "    # create dataset\n",
-        "    create_dataset_op = TabularDatasetCreateOp(\n",
+        "    create_dataset_op = vertex_ai_components.TabularDatasetCreateOp(\n",
        "        display_name=dataset_name,\n",
        "        gcs_source=dataset_uri,\n",
        "        project=project_id,\n",
@@ -2480,6 +2220,7 @@
        "        project=project_id,\n",
        "        location=location,\n",
        "        container_image=custom_container_image,\n",
+        "        batch_id=training_batch_id,\n",
        "        main_python_file_uri=training_main_python_file_uri,\n",
        "        args=build_training_args_op.output,\n",
        "    ).after(build_training_args_op)\n",
@@ -2492,12 +2233,11 @@
        "        name=AUPR_HYPERTUNE_CONDITION,\n",
        "    ):\n",
        "\n",
-        "        build_hpt_args_op = build_hpt_args(\n",
+        "        build_hpt_args_op = build_training_args(\n",
        "            dataset_uri=create_dataset_op.output,\n",
        "            train_path=train_path,\n",
        "            model_path=hpt_model_path,\n",
        "            metrics_path=hpt_metrics_path,\n",
-        "            bundle_path=hpt_bundle_path,\n",
        "        ).after(evaluate_model_op)\n",
        "\n",
        "        # hyperparameter tuning\n",
@@ -2505,46 +2245,13 @@
        "            project=project_id,\n",
        "            location=location,\n",
        "            container_image=custom_container_image,\n",
+        "            batch_id=hpt_batch_id,\n",
        "            main_python_file_uri=hpt_main_python_file_uri,\n",
        "            args=build_hpt_args_op.output,\n",
-        "            runtime_config_properties=HPT_RUNTIME_PROPERTIES,\n",
        "        ).after(model_traning_op)\n",
        "\n",
-        "        # evaluate condition to upload and deploy model to Vertex AI\n",
-        "        with Condition(\n",
-        "            # kfp casts `bool` parameter to `str`\n",
-        "            deploy_model == \"True\",\n",
-        "            name=DEPLOY_MODEL_CONDITION,\n",
-        "        ):\n",
-        "            # import the model into the pipeline as a kfp model artifact\n",
-        "            import_model_artifact_op = import_model_artifact(\n",
-        "                artifact_uri=artifact_uri,\n",
-        "                serving_image_uri=serving_image_uri,\n",
-        "            )\n",
-        "\n",
-        "            # upload model to Vertex AI\n",
-        "            model_upload_op = ModelUploadOp(\n",
-        "                project=project_id,\n",
-        "                location=location,\n",
-        "                display_name=model_name,\n",
-        "                unmanaged_container_model=import_model_artifact_op.outputs[\"model\"],\n",
-        "            ).after(hyperparameter_tuning_op)\n",
-        "\n",
-        "            # create a serving endpoint\n",
-        "            endpoint_op = EndpointCreateOp(\n",
-        "                project=project_id,\n",
-        "                location=location,\n",
-        "                display_name=model_name,\n",
-        "            ).after(model_upload_op)\n",
-        "\n",
-        "            # deploy  model to the serving endpoint\n",
-        "            _ = ModelDeployOp(\n",
-        "                model=model_upload_op.outputs[\"model\"],\n",
-        "                endpoint=endpoint_op.outputs[\"endpoint\"],\n",
-        "                dedicated_resources_machine_type=\"n1-standard-2\",\n",
-        "                dedicated_resources_min_replica_count=1,\n",
-        "                dedicated_resources_max_replica_count=1,\n",
-        "            ).after(endpoint_op)"
+        "        # upload model\n",
+        "        register_model(artifact_uri=hpt_model_path).after(hyperparameter_tuning_op)"
      ]
    },
    {
@@ -2620,65 +2327,6 @@
        "pipeline.wait()"
      ]
    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "b584afa5a1b1"
-      },
-      "source": [
-        "### (Optional) Get online predictions from the deployed model\n",
-        "\n",
-        "You can request online predictions if the model was deployed to a Vertex AI endpoint. Use the `google-cloud-aiplatform` client library to request predictions, or use `curl` as per below:\n",
-        "\n",
-        "Create the prediction request payload with the instances that you want to predict:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "12d068e1877c"
-      },
-      "outputs": [],
-      "source": [
-        "%%writefile instances.json\n",
-        "{\n",
-        "    \"instances\": [\n",
-        "        [214.0, \"360\", \"Rural\", 2.13, 2.21, 0.0, 0.0, 2.31, 2.01, 0.0, 0.0, 0.0, 0.0],\n",
-        "        [213.0, \"360\", \"Semiurban\", 2.03, 2.11, 0.0, 0.0, 2.13, 2.02, 0.0, 0.0, 0.0, 0.0]\n",
-        "    ]\n",
-        "}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "b7cbfec4537d"
-      },
-      "source": [
-        "Use `curl` to send the prediction request to the Vertex AI endpoint. The response contains the predicted label (`0 == not eligible`, `1 == eligible`) for each instance sent in the request payload."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "b1617d6e8a3d"
-      },
-      "outputs": [],
-      "source": [
-        "ENDPOINT_ID=!(gcloud ai endpoints list \\\n",
-        "              --region={REGION} \\\n",
-        "              --filter=display_name={MODEL_NAME} \\\n",
-        "              --format='value(name)')\n",
-        "\n",
-        "!curl -X POST \\\n",
-        "   -H \"Authorization: Bearer $(gcloud auth print-access-token)\" \\\n",
-        "   -H \"Content-Type: application/json\" \\\n",
-        "   https://{REGION}-aiplatform.googleapis.com/v1/projects/{PROJECT_ID}/locations/us-central1/endpoints/{ENDPOINT_ID[-1]}:predict \\\n",
-        "   -d \"@instances.json\""
-      ]
-    },
    {
      "cell_type": "markdown",
      "metadata": {
@@ -2704,14 +2352,8 @@
        "# Delete pipeline\n",
        "pipeline.delete()\n",
        "\n",
-        "# Delete endpoints\n",
-        "endpoint_list = vertex_ai.Endpoint.list(filter=f'display_name=\"{MODEL_NAME}\"')\n",
-        "for endpoint in endpoint_list:\n",
-        "    endpoint.undeploy_all()\n",
-        "    endpoint.delete()\n",
-        "\n",
        "# Delete model\n",
-        "model_list = vertex_ai.Model.list(filter=f'display_name=\"{MODEL_NAME}\"')\n",
+        "model_list = vertex_ai.TabularDataset.list(filter=f'display_name=\"{MODEL_NAME}\"')\n",
        "for model in model_list:\n",
        "    model.delete()\n",
        "\n",
@@ -64,30 +64,6 @@
        "This notebook shows how to use [the Kubeflow Pipelines (KFP) SDK](https://www.kubeflow.org/docs/components/pipelines/) to build [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines) that generate model metrics and metrics visualizations, and comparing pipeline runs."
      ]
    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "objective:pipelines,metrics"
-      },
-      "source": [
-        "### Objective\n",
-        "\n",
-        "In this tutorial, you use the KFP SDK to build pipelines that generate evaluation metrics.\n",
-        "\n",
-        "This tutorial uses the following Google Cloud ML services:\n",
-        "\n",
-        "- `Vertex AI Pipelines`\n",
-        "\n",
-        "The steps performed include:\n",
-        "\n",
-        "- Create KFP components:\n",
-        "    - Generate ROC curve and confusion matrix visualizations for classification results\n",
-        "    - Write metrics\n",
-        "- Create KFP pipelines.\n",
-        "- Execute KFP pipelines\n",
-        "- Compare metrics across pipeline runs"
-      ]
-    },
    {
      "cell_type": "markdown",
      "metadata": {
@@ -112,6 +88,30 @@
        "The dataset predicts the type of Iris flower species from a class of three species: setosa, virginica, or versicolor."
      ]
    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "objective:pipelines,metrics"
+      },
+      "source": [
+        "### Objective\n",
+        "\n",
+        "In this tutorial, you use the KFP SDK to build pipelines that generate evaluation metrics.\n",
+        "\n",
+        "This tutorial uses the following Google Cloud ML services:\n",
+        "\n",
+        "- `Vertex AI Pipelines`\n",
+        "\n",
+        "The steps performed include:\n",
+        "\n",
+        "- Create KFP components:\n",
+        "    - Generate ROC curve and confusion matrix visualizations for classification results\n",
+        "    - Write metrics\n",
+        "- Create KFP pipelines.\n",
+        "- Execute KFP pipelines\n",
+        "- Compare metrics across pipeline runs"
+      ]
+    },
    {
      "cell_type": "markdown",
      "metadata": {
@@ -202,7 +202,7 @@
        "! pip3 install {USER_FLAG} kfp google-cloud-pipeline-components --upgrade -q\n",
        "\n",
        "if os.getenv(\"IS_TESTING\"):\n",
-        "    ! pip3 install --upgrade matplotlib $USER_FLAG -q"
+        "    ! pip3 install --upgrade matplotlib $USER_FLAG"
      ]
    },
    {
@@ -240,8 +240,6 @@
        "id": "check_versions"
      },
      "source": [
-        "### KFP SDK version\n",
-        "\n",
        "Check the versions of the packages you installed.  The KFP SDK version should be >=1.6."
      ]
    },
@@ -351,10 +349,7 @@
      },
      "outputs": [],
      "source": [
-        "REGION = \"[your-region]\"  # @param {type: \"string\"}\n",
-        "\n",
-        "if REGION == \"[your-region]\":\n",
-        "    REGION = \"us-central1\""
+        "REGION = \"us-central1\"  # @param {type: \"string\"}"
      ]
    },
    {
@@ -363,9 +358,9 @@
        "id": "timestamp"
      },
      "source": [
-        "#### UUID\n",
+        "#### Timestamp\n",
        "\n",
-        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
+        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append the timestamp onto the name of resources you create in this tutorial."
      ]
    },
    {
@@ -376,16 +371,9 @@
      },
      "outputs": [],
      "source": [
-        "import random\n",
-        "import string\n",
+        "from datetime import datetime\n",
        "\n",
-        "\n",
-        "# Generate a uuid of a specifed length(default=8)\n",
-        "def generate_uuid(length: int = 8) -> str:\n",
-        "    return \"\".join(random.choices(string.ascii_lowercase + string.digits, k=length))\n",
-        "\n",
-        "\n",
-        "UUID = generate_uuid()"
+        "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
      ]
    },
    {
@@ -396,7 +384,7 @@
      "source": [
        "### Authenticate your Google Cloud account\n",
        "\n",
-        "**If you are using Vertex AI Workbench Notebook**, your environment is already authenticated.\n",
+        "**If you are using Vertex AI Workbench Notebook**, your environment is already authenticated. Skip this step.\n",
        "\n",
        "**If you are using Colab**, run the cell below and follow the instructions\n",
        "when prompted to authenticate your account via oAuth.\n",
@@ -491,7 +479,7 @@
      "outputs": [],
      "source": [
        "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"[your-bucket-name]\":\n",
-        "    BUCKET_NAME = PROJECT_ID + \"aip-\" + UUID\n",
+        "    BUCKET_NAME = PROJECT_ID + \"aip-\" + TIMESTAMP\n",
        "    BUCKET_URI = \"gs://\" + BUCKET_NAME"
      ]
    },
@@ -565,10 +553,6 @@
      },
      "outputs": [],
      "source": [
-        "import sys\n",
-        "\n",
-        "IS_COLAB = \"google.colab\" in sys.modules\n",
-        "\n",
        "if (\n",
        "    SERVICE_ACCOUNT == \"\"\n",
        "    or SERVICE_ACCOUNT is None\n",
@@ -928,12 +912,12 @@
      },
      "outputs": [],
      "source": [
-        "DISPLAY_NAME = \"iris_\" + UUID\n",
+        "DISPLAY_NAME = \"iris_\" + TIMESTAMP\n",
        "\n",
        "job = aip.PipelineJob(\n",
        "    display_name=DISPLAY_NAME,\n",
        "    template_path=\"tabular classification_pipeline.json\".replace(\" \", \"_\"),\n",
-        "    job_id=f\"tabular classification-v2{UUID}-1\".replace(\" \", \"\"),\n",
+        "    job_id=f\"tabular classification-v2{TIMESTAMP}-1\".replace(\" \", \"\"),\n",
        "    pipeline_root=PIPELINE_ROOT,\n",
        "    parameter_values={\"seed\": 7, \"splits\": 10},\n",
        ")\n",
@@ -966,18 +950,7 @@
        "\n",
        "Next, generate another pipeline run that uses a different `seed` and `split` for the `iris_logregression` step.\n",
        "\n",
-        "Submit the new pipeline run:\n",
-        "\n",
-        "\n",
-        "**pipeline_root :**   Specify a Cloud Storage URI that your pipelines service account can access. The artifacts of your pipeline runs are stored within the pipeline root. \n",
-        "\n",
-        "**display_name :**  The name of the pipeline, this will show up in the Google Cloud console. \n",
-        "\n",
-        "**parameter_values :**  The pipeline parameters to pass to this run. For example, create a dict() with the parameter names as the dictionary keys and the parameter values as the dictionary values. \n",
-        "\n",
-        "**job_id :**  A unique identifier for this pipeline run. If the job ID is not specified, Vertex AI Pipelines creates a job ID for you using the pipeline name and the timestamp of when the pipeline run was started. \n",
-        "\n",
-        "**template_path :**  complete pipeline path"
+        "Submit the new pipeline run:"
      ]
    },
    {
@@ -989,9 +962,9 @@
      "outputs": [],
      "source": [
        "job = aip.PipelineJob(\n",
-        "    display_name=\"iris_\" + UUID,\n",
+        "    display_name=\"iris_\" + TIMESTAMP,\n",
        "    template_path=\"tabular classification_pipeline.json\".replace(\" \", \"_\"),\n",
-        "    job_id=f\"tabular classification-pipeline-v2{UUID}-2\".replace(\" \", \"\"),\n",
+        "    job_id=f\"tabular classification-pipeline-v2{TIMESTAMP}-2\".replace(\" \", \"\"),\n",
        "    pipeline_root=PIPELINE_ROOT,\n",
        "    parameter_values={\"seed\": 5, \"splits\": 7},\n",
        ")\n",
@@ -1108,7 +1081,16 @@
        "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n",
        "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n",
        "\n",
-        "Otherwise, you can delete the individual resources you created in this tutorial -- *Note:* this is auto-generated and not all resources may be applicable for this tutorial:"
+        "Otherwise, you can delete the individual resources you created in this tutorial -- *Note:* this is auto-generated and not all resources may be applicable for this tutorial:\n",
+        "\n",
+        "- Dataset\n",
+        "- Pipeline\n",
+        "- Model\n",
+        "- Endpoint\n",
+        "- Batch Job\n",
+        "- Custom Job\n",
+        "- Hyperparameter Tuning Job\n",
+        "- Cloud Storage Bucket"
      ]
    },
    {
@@ -1119,9 +1101,94 @@
      },
      "outputs": [],
      "source": [
-        "delete_bucket = False\n",
+        "delete_dataset = True\n",
+        "delete_pipeline = True\n",
+        "delete_model = True\n",
+        "delete_endpoint = True\n",
+        "delete_batchjob = True\n",
+        "delete_customjob = True\n",
+        "delete_hptjob = True\n",
+        "delete_bucket = True\n",
+        "\n",
+        "try:\n",
+        "    if delete_model and \"DISPLAY_NAME\" in globals():\n",
+        "        models = aip.Model.list(\n",
+        "            filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
+        "        )\n",
+        "        model = models[0]\n",
+        "        aip.Model.delete(model)\n",
+        "        print(\"Deleted model:\", model)\n",
+        "except Exception as e:\n",
+        "    print(e)\n",
+        "\n",
+        "try:\n",
+        "    if delete_endpoint and \"DISPLAY_NAME\" in globals():\n",
+        "        endpoints = aip.Endpoint.list(\n",
+        "            filter=f\"display_name={DISPLAY_NAME}_endpoint\", order_by=\"create_time\"\n",
+        "        )\n",
+        "        endpoint = endpoints[0]\n",
+        "        endpoint.undeploy_all()\n",
+        "        aip.Endpoint.delete(endpoint.resource_name)\n",
+        "        print(\"Deleted endpoint:\", endpoint)\n",
+        "except Exception as e:\n",
+        "    print(e)\n",
+        "\n",
+        "if delete_dataset and \"DISPLAY_NAME\" in globals():\n",
+        "    if \"tabular\" == \"tabular\":\n",
+        "        try:\n",
+        "            datasets = aip.TabularDataset.list(\n",
+        "                filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
+        "            )\n",
+        "            dataset = datasets[0]\n",
+        "            aip.TabularDataset.delete(dataset.resource_name)\n",
+        "            print(\"Deleted dataset:\", dataset)\n",
+        "        except Exception as e:\n",
+        "            print(e)\n",
+        "\n",
+        "    if \"tabular\" == \"image\":\n",
+        "        try:\n",
+        "            datasets = aip.ImageDataset.list(\n",
+        "                filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
+        "            )\n",
+        "            dataset = datasets[0]\n",
+        "            aip.ImageDataset.delete(dataset.resource_name)\n",
+        "            print(\"Deleted dataset:\", dataset)\n",
+        "        except Exception as e:\n",
+        "            print(e)\n",
+        "\n",
+        "    if \"tabular\" == \"text\":\n",
+        "        try:\n",
+        "            datasets = aip.TextDataset.list(\n",
+        "                filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
+        "            )\n",
+        "            dataset = datasets[0]\n",
+        "            aip.TextDataset.delete(dataset.resource_name)\n",
+        "            print(\"Deleted dataset:\", dataset)\n",
+        "        except Exception as e:\n",
+        "            print(e)\n",
+        "\n",
+        "    if \"tabular\" == \"video\":\n",
+        "        try:\n",
+        "            datasets = aip.VideoDataset.list(\n",
+        "                filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
+        "            )\n",
+        "            dataset = datasets[0]\n",
+        "            aip.VideoDataset.delete(dataset.resource_name)\n",
+        "            print(\"Deleted dataset:\", dataset)\n",
+        "        except Exception as e:\n",
+        "            print(e)\n",
+        "\n",
+        "try:\n",
+        "    if delete_pipeline and \"DISPLAY_NAME\" in globals():\n",
+        "        pipelines = aip.PipelineJob.list(\n",
+        "            filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
+        "        )\n",
+        "        pipeline = pipelines[0]\n",
+        "        aip.PipelineJob.delete(pipeline.resource_name)\n",
+        "        print(\"Deleted pipeline:\", pipeline)\n",
+        "except Exception as e:\n",
+        "    print(e)\n",
        "\n",
-        "job.delete()\n",
        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
        "    ! gsutil rm -r $BUCKET_URI"
      ]
@@ -1,5 +1,14 @@
 {
  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c4b363e1330b"
+      },
+      "source": [
+        "# Build a fraud detection model on Vertex AI"
+      ]
+    },
    {
      "cell_type": "code",
      "execution_count": null,
@@ -29,8 +38,6 @@
        "id": "05c670d35496"
      },
      "source": [
-        "# Build a fraud detection model on Vertex AI\n",
-        "\n",
        "<table align=\"left\">\n",
        "\n",
        "  <td>\n",
@@ -53,6 +60,28 @@
        "</table>"
      ]
    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4c5fb7f2090f"
+      },
+      "source": [
+        "## Table of contents\n",
+        "\n",
+        "* [Overview](#section-1)\n",
+        "* [Dataset](#section-2)\n",
+        "* [Objective](#section-3)\n",
+        "* [Costs](#section-4)\n",
+        "* [Analyze the dataset](#section-5)\n",
+        "* [Fit a random forest model](#section-6)\n",
+        "* [Analyzing results](#section-7)\n",
+        "* [Save the model to a Cloud Storagae path](#section-8)\n",
+        "* [Create a model in Vertex AI](#section-9)\n",
+        "* [Create an Endpoint](#section-10)  \n",
+        "* [What-If Tool ](#section-11)\n",
+        "* [Clean up](#section-12)"
+      ]
+    },
    {
      "cell_type": "markdown",
      "metadata": {
@@ -65,6 +94,19 @@
        "This tutorial shows you how to build, deploy, and analyze predictions from a simple [random forest](https://en.wikipedia.org/wiki/Random_forest) model using tools like scikit-learn, Vertex AI, and the [What-IF Tool (WIT)](https://cloud.google.com/ai-platform/prediction/docs/using-what-if-tool) on a synthetic fraud transaction dataset to solve a financial fraud detection problem.\n"
      ]
    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9625185ccee9"
+      },
+      "source": [
+        "## Dataset\n",
+        "<a name=\"section-2\"></a>\n",
+        "\n",
+        "\n",
+        "The dataset used in this tutorial is publicly available at Kaggle. See [Synthetic Financial Datasets For Fraud Detection](https://www.kaggle.com/ealaxi/paysim1)."
+      ]
+    },
    {
      "cell_type": "markdown",
      "metadata": {
@@ -76,13 +118,6 @@
        "\n",
        "This tutorial demonstrates data analysis and model-building using a synthetic financial dataset. The model is trained on identifying fraudulent cases among the transactions. Then, the trained model is deployed on a Vertex AI Endpoint and analyzed using the What-If Tool. The steps taken in this tutorial are as follows: \n",
        "\n",
-        "This tutorial uses the following Google Cloud ML services and resources:\n",
-        "\n",
-        "- Vertex AI Model\n",
-        "- Vertex AI Endpoint\n",
-        "\n",
-        "The steps performed include:\n",
-        "\n",
        "- Installation of required libraries\n",
        "- Reading the dataset from a Cloud Storage bucket\n",
        "- Performing exploratory analysis on the dataset\n",
@@ -94,19 +129,6 @@
        "- Un-deploying the model and cleaning up the model resources"
      ]
    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "3037523e7523"
-      },
-      "source": [
-        "## Dataset\n",
-        "<a name=\"section-2\"></a>\n",
-        "\n",
-        "\n",
-        "The dataset used in this tutorial is publicly available at Kaggle. See [Synthetic Financial Datasets For Fraud Detection](https://www.kaggle.com/ealaxi/paysim1)."
-      ]
-    },
    {
      "cell_type": "markdown",
      "metadata": {
@@ -132,15 +154,21 @@
    {
      "cell_type": "markdown",
      "metadata": {
-        "id": "cd1bc75a1cb2"
+        "id": "1ba37fa1511f"
      },
      "source": [
        "### Set up your local development environment\n",
        "\n",
        "**If you are using Colab or Google Cloud Notebooks**, your environment already meets\n",
-        "all the requirements to run this notebook. You can skip this step.\n",
-        "\n",
-        "\n",
+        "all the requirements to run this notebook. You can skip this step."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cd1bc75a1cb2"
+      },
+      "source": [
        "**Otherwise**, make sure your environment meets this notebook's requirements.\n",
        "You need the following:\n",
        "\n",
@@ -183,44 +211,142 @@
    },
    {
      "cell_type": "code",
-      "execution_count": 1,
+      "execution_count": null,
      "metadata": {
        "id": "172533a994ad"
      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
-            "flake8 4.0.1 requires importlib-metadata<4.3; python_version < \"3.8\", but you have importlib-metadata 4.12.0 which is incompatible.\u001b[0m\u001b[31m\n",
-            "\u001b[0m"
-          ]
-        }
-      ],
+      "outputs": [],
      "source": [
        "import os\n",
        "\n",
-        "# The Vertex AI Workbench Notebook product has specific requirements\n",
-        "IS_WORKBENCH_NOTEBOOK = os.getenv(\"DL_ANACONDA_HOME\")\n",
-        "IS_USER_MANAGED_WORKBENCH_NOTEBOOK = os.path.exists(\n",
-        "    \"/opt/deeplearning/metadata/env_version\"\n",
-        ")\n",
+        "import google.auth\n",
        "\n",
-        "# Vertex AI Notebook requires dependencies to be installed with '--user'\n",
        "USER_FLAG = \"\"\n",
-        "if IS_WORKBENCH_NOTEBOOK:\n",
-        "    USER_FLAG = \"--user\"\n",
+        "# Google Cloud Notebook requires dependencies to be installed with '--user'\n",
+        "if \"default\" in dir(google.auth):\n",
+        "    USER_FLAG = \"--user\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "a465cf9367de"
+      },
+      "source": [
+        "Install the latest version of the Vertex AI client library.\n",
        "\n",
-        "# Install the latest version of the Vertex AI client library.\n",
-        "! pip3 install --upgrade google-cloud-aiplatform {USER_FLAG} -q\n",
-        "\n",
-        "# Install additional libraries\n",
-        "! pip3 install {USER_FLAG} witwidget -q\n",
-        "! pip3 install {USER_FLAG} joblib -q\n",
-        "! pip3 install {USER_FLAG} scikit-learn -q\n",
-        "! pip3 install {USER_FLAG} fsspec -q\n",
-        "! pip3 install {USER_FLAG} gcsfs -q"
+        "Run the following command in your notebook environment to install the Vertex SDK for Python:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6380f7ee5f54"
+      },
+      "outputs": [],
+      "source": [
+        "! pip install {USER_FLAG} --upgrade google-cloud-aiplatform"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1969a1cc46cf"
+      },
+      "source": [
+        "Run the following command in your notebook environment to install witwidget:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8b10e59b0911"
+      },
+      "outputs": [],
+      "source": [
+        "! pip install {USER_FLAG} witwidget"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4099ce79705a"
+      },
+      "source": [
+        "Run the following command in your notebook environment to install joblib:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1e56d524753a"
+      },
+      "outputs": [],
+      "source": [
+        "! pip install {USER_FLAG} joblib"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b87ee3041f7d"
+      },
+      "source": [
+        "Run the following command in your notebook environment to install scikit-learn:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "c3ebecd9bd72"
+      },
+      "outputs": [],
+      "source": [
+        "! pip install {USER_FLAG} scikit-learn"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b624b5163531"
+      },
+      "source": [
+        "Run the following command in your notebook environment to install fsspec:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "79c7a64b04de"
+      },
+      "outputs": [],
+      "source": [
+        "! pip install {USER_FLAG} fsspec"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5593090dcf0a"
+      },
+      "source": [
+        "Run the following command in your notebook environment to install gcsfs:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7bf981bc5bf6"
+      },
+      "outputs": [],
+      "source": [
+        "! pip install {USER_FLAG} gcsfs"
      ]
    },
    {
@@ -252,14 +378,21 @@
        "    app.kernel.do_shutdown(True)"
      ]
    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2d9b3731b3e0"
+      },
+      "source": [
+        "## Before you begin"
+      ]
+    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7a5cb1df1ef7"
      },
      "source": [
-        "## Before you begin\n",
-        "\n",
        "### Set up your Google Cloud project\n",
        "\n",
        "**The following steps are required, regardless of your notebook environment.**\n",
@@ -293,26 +426,19 @@
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
-        "id": "dcdfccf50581"
+        "id": "b27f37ed1ccf"
      },
      "outputs": [],
      "source": [
-        "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "5bf9979b96ff"
-      },
-      "outputs": [],
-      "source": [
-        "if PROJECT_ID == \"\" or PROJECT_ID is None or PROJECT_ID == \"[your-project-id]\":\n",
-        "    # Get your GCP project id from gcloud\n",
-        "    shell_output = ! gcloud config list --format 'value(core.project)' 2>/dev/null\n",
+        "import os\n",
+        "\n",
+        "PROJECT_ID = \"\"\n",
+        "\n",
+        "# Get your Google Cloud project ID from gcloud\n",
+        "if not os.getenv(\"IS_TESTING\"):\n",
+        "    shell_output = !gcloud config list --format 'value(core.project)' 2>/dev/null\n",
        "    PROJECT_ID = shell_output[0]\n",
-        "    print(\"Project ID:\", PROJECT_ID)"
+        "    print(\"Project ID: \", PROJECT_ID)"
      ]
    },
    {
@@ -324,6 +450,18 @@
        "Otherwise, set your project ID here."
      ]
    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3dbdf6a5c539"
+      },
+      "outputs": [],
+      "source": [
+        "if PROJECT_ID == \"\" or PROJECT_ID is None:\n",
+        "    PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}"
+      ]
+    },
    {
      "cell_type": "code",
      "execution_count": null,
@@ -335,49 +473,15 @@
        "! gcloud config set project $PROJECT_ID"
      ]
    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "264543a144ad"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable, which is used for operations\n",
-        "throughout the rest of this notebook.  Below are regions supported for Vertex AI. It is recommended that you choose the region closest to you.\n",
-        "\n",
-        "- Americas: `us-central1`\n",
-        "- Europe: `europe-west4`\n",
-        "- Asia Pacific: `asia-east1`\n",
-        "\n",
-        "You may not use a multi-regional bucket for training with Vertex AI. Not all regions provide support for all Vertex AI services.\n",
-        "\n",
-        "Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "3281bedf6d3c"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"[your-region]\"  # @param {type: \"string\"}\n",
-        "\n",
-        "if REGION == \"[your-region]\":\n",
-        "    REGION = \"us-central1\""
-      ]
-    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e663bd062c6f"
      },
      "source": [
-        "#### UUID\n",
+        "#### Timestamp\n",
        "\n",
-        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
+        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append it onto the name of resources you create in this tutorial."
      ]
    },
    {
@@ -388,16 +492,21 @@
      },
      "outputs": [],
      "source": [
-        "import random\n",
-        "import string\n",
+        "from datetime import datetime\n",
        "\n",
+        "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0c7f603fcdcf"
+      },
+      "source": [
+        "### Authenticate your Google Cloud account\n",
        "\n",
-        "# Generate a uuid of length 8\n",
-        "def generate_uuid():\n",
-        "    return \"\".join(random.choices(string.ascii_lowercase + string.digits, k=8))\n",
-        "\n",
-        "\n",
-        "UUID = generate_uuid()"
+        "**If you are using Google Cloud Notebooks**, your environment is already\n",
+        "authenticated. Skip this step."
      ]
    },
    {
@@ -406,11 +515,6 @@
        "id": "72bf8f7c9ab3"
      },
      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "**If you are using Vertex AI Workbench Notebooks**, your environment is already\n",
-        "authenticated. Skip this step.\n",
-        "\n",
        "**If you are using Colab**, run the cell below and follow the instructions\n",
        "when prompted to authenticate your account via oAuth.\n",
        "\n",
@@ -443,19 +547,19 @@
      },
      "outputs": [],
      "source": [
+        "import os\n",
+        "import sys\n",
+        "\n",
        "# If you are running this notebook in Colab, run this cell and follow the\n",
        "# instructions to authenticate your GCP account. This provides access to your\n",
        "# Cloud Storage bucket and lets you submit training jobs and prediction\n",
        "# requests.\n",
        "\n",
-        "import os\n",
-        "import sys\n",
+        "# The Google Cloud Notebook product has specific requirements\n",
+        "IS_GOOGLE_CLOUD_NOTEBOOK = os.path.exists(\"/opt/deeplearning/metadata/env_version\")\n",
        "\n",
-        "# If on Vertex AI Workbench, then don't execute this code\n",
-        "IS_COLAB = \"google.colab\" in sys.modules\n",
-        "if not os.path.exists(\"/opt/deeplearning/metadata/env_version\") and not os.getenv(\n",
-        "    \"DL_ANACONDA_HOME\"\n",
-        "):\n",
+        "# If on Google Cloud Notebooks, then don't execute this code\n",
+        "if not IS_GOOGLE_CLOUD_NOTEBOOK:\n",
        "    if \"google.colab\" in sys.modules:\n",
        "        from google.colab import auth as google_auth\n",
        "\n",
@@ -497,25 +601,27 @@
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
-        "id": "5e9a782f5608"
+        "id": "f56c52ba662c"
      },
      "outputs": [],
      "source": [
        "BUCKET_NAME = \"[your-bucket-name]\"  # @param {type:\"string\"}\n",
-        "BUCKET_URI = f\"gs://{BUCKET_NAME}\""
+        "REGION = \"[your-region]\"  # @param {type:\"string\"}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
-        "id": "6d0729c4ae94"
+        "id": "68d1f4908641"
      },
      "outputs": [],
      "source": [
        "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"[your-bucket-name]\":\n",
-        "    BUCKET_NAME = PROJECT_ID + \"aip-\" + UUID\n",
-        "    BUCKET_URI = f\"gs://{BUCKET_NAME}\""
+        "    BUCKET_NAME = PROJECT_ID + \"-vertex-ai-\" + TIMESTAMP\n",
+        "BUCKET_URI = f\"gs://{BUCKET_NAME}\"\n",
+        "if REGION == \"[your-region]\":\n",
+        "    REGION = \"us-central1\""
      ]
    },
    {
@@ -591,7 +697,6 @@
        "import numpy as np\n",
        "import pandas as pd\n",
        "from google.cloud import aiplatform, storage\n",
-        "from IPython.display import display\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import (average_precision_score, classification_report,\n",
        "                             confusion_matrix, f1_score)\n",
@@ -601,15 +706,6 @@
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "fdcb614c716f"
-      },
-      "source": [
-        "## Load dataset"
-      ]
-    },
    {
      "cell_type": "code",
      "execution_count": null,
@@ -618,6 +714,7 @@
      },
      "outputs": [],
      "source": [
+        "# Load dataset\n",
        "df = pd.read_csv(\n",
        "    \"gs://cloud-samples-data/vertex-ai/managed_notebooks/fraud_detection/fraud_detection_data.csv\"\n",
        ")"
@@ -934,8 +1031,6 @@
        "\n",
        "# Upload the saved model file to Cloud Storage\n",
        "BLOB_PATH = \"[your-blob-path]\"\n",
-        "if BLOB_PATH == \"[your-blob-path]\":\n",
-        "    BLOB_PATH = \"fraud-detection-model-path\"\n",
        "BLOB_NAME = os.path.join(BLOB_PATH, FILE_NAME)\n",
        "\n",
        "bucket = storage.Client(PROJECT_ID).bucket(BUCKET_NAME)\n",
@@ -962,8 +1057,6 @@
      "outputs": [],
      "source": [
        "MODEL_DISPLAY_NAME = \"[your-model-display-name]\"\n",
-        "if MODEL_DISPLAY_NAME == \"[your-model-display-name]\":\n",
-        "    MODEL_DISPLAY_NAME = \"fraud-detection-model-display-name\"\n",
        "ARTIFACT_GCS_PATH = f\"{BUCKET_URI}/{BLOB_PATH}\"\n",
        "SERVING_CONTAINER_IMAGE_URI = (\n",
        "    \"us-docker.pkg.dev/vertex-ai/prediction/sklearn-cpu.1-0:latest\"\n",
@@ -1012,9 +1105,7 @@
      },
      "outputs": [],
      "source": [
-        "ENDPOINT_DISPLAY_NAME = \"[your-endpoint-display-name]\"\n",
-        "if ENDPOINT_DISPLAY_NAME == \"[your-endpoint-display-name]\":\n",
-        "    ENDPOINT_DISPLAY_NAME = \"fraud-detection-endpoint\""
+        "ENDPOINT_DISPLAY_NAME = \"[your-endpoint-display-name]\""
      ]
    },
    {
@@ -1052,8 +1143,6 @@
      "outputs": [],
      "source": [
        "DEPLOYED_MODEL_NAME = \"[your-deployed-model-name]\"\n",
-        "if DEPLOYED_MODEL_NAME == \"[your-deployed-model-name]\":\n",
-        "    DEPLOYED_MODEL_NAME = \"fraud-detection-deployed-model\"\n",
        "MACHINE_TYPE = \"n1-standard-2\""
      ]
    },
@@ -1135,33 +1224,34 @@
      },
      "outputs": [],
      "source": [
-        "if not IS_COLAB:\n",
-        "    # define target and labels\n",
-        "    TARGET_FEATURE = \"isFraud\"\n",
-        "    LABEL_VOCAB = [\"not-fraud\", \"fraud\"]\n",
+        "# define target and labels\n",
+        "TARGET_FEATURE = \"isFraud\"\n",
+        "LABEL_VOCAB = [\"not-fraud\", \"fraud\"]\n",
        "\n",
-        "    # define the function to adjust the predictions\n",
+        "# define the function to adjust the predictions\n",
        "\n",
-        "    def adjust_prediction(pred):\n",
-        "        return [1 - pred, pred]\n",
        "\n",
-        "    # Combine the features and labels into one array for the What-If Tool\n",
-        "    test_examples = np.hstack(\n",
-        "        (test_samples_X.to_numpy(), test_samples_y.to_numpy().reshape(-1, 1))\n",
+        "def adjust_prediction(pred):\n",
+        "    return [1 - pred, pred]\n",
+        "\n",
+        "\n",
+        "# Combine the features and labels into one array for the What-If Tool\n",
+        "test_examples = np.hstack(\n",
+        "    (test_samples_X.to_numpy(), test_samples_y.to_numpy().reshape(-1, 1))\n",
+        ")\n",
+        "\n",
+        "# Configure the WIT to run on the locally trained model\n",
+        "config_builder = (\n",
+        "    WitConfigBuilder(\n",
+        "        test_examples.tolist(), test_samples_X.columns.tolist() + [\"isFraud\"]\n",
        "    )\n",
+        "    .set_custom_predict_fn(forest.predict_proba)\n",
+        "    .set_target_feature(TARGET_FEATURE)\n",
+        "    .set_label_vocab(LABEL_VOCAB)\n",
+        ")\n",
        "\n",
-        "    # Configure the WIT to run on the locally trained model\n",
-        "    config_builder = (\n",
-        "        WitConfigBuilder(\n",
-        "            test_examples.tolist(), test_samples_X.columns.tolist() + [\"isFraud\"]\n",
-        "        )\n",
-        "        .set_custom_predict_fn(forest.predict_proba)\n",
-        "        .set_target_feature(TARGET_FEATURE)\n",
-        "        .set_label_vocab(LABEL_VOCAB)\n",
-        "    )\n",
-        "\n",
-        "    # display the WIT widget\n",
-        "    display(WitWidget(config_builder, height=600))"
+        "# display the WIT widget\n",
+        "WitWidget(config_builder, height=600)"
      ]
    },
    {
@@ -1181,35 +1271,36 @@
      },
      "outputs": [],
      "source": [
-        "if not IS_COLAB:\n",
-        "    # configure the target and class-labels\n",
-        "    TARGET_FEATURE = \"isFraud\"\n",
-        "    LABEL_VOCAB = [\"not-fraud\", \"fraud\"]\n",
+        "# configure the target and class-labels\n",
+        "TARGET_FEATURE = \"isFraud\"\n",
+        "LABEL_VOCAB = [\"not-fraud\", \"fraud\"]\n",
        "\n",
-        "    # function to return predictions from the deployed Model\n",
+        "# function to return predictions from the deployed Model\n",
        "\n",
-        "    def endpoint_predict_sample(instances: list):\n",
-        "        prediction = endpoint.predict(instances=instances)\n",
-        "        preds = [[1 - i, i] for i in prediction.predictions]\n",
-        "        return preds\n",
        "\n",
-        "    # Combine the features and labels into one array for the What-If Tool\n",
-        "    test_examples = np.hstack(\n",
-        "        (test_samples_X.to_numpy(), test_samples_y.to_numpy().reshape(-1, 1))\n",
+        "def endpoint_predict_sample(instances: list):\n",
+        "    prediction = endpoint.predict(instances=instances)\n",
+        "    preds = [[1 - i, i] for i in prediction.predictions]\n",
+        "    return preds\n",
+        "\n",
+        "\n",
+        "# Combine the features and labels into one array for the What-If Tool\n",
+        "test_examples = np.hstack(\n",
+        "    (test_samples_X.to_numpy(), test_samples_y.to_numpy().reshape(-1, 1))\n",
+        ")\n",
+        "\n",
+        "# Configure the WIT with the prediction function\n",
+        "config_builder = (\n",
+        "    WitConfigBuilder(\n",
+        "        test_examples.tolist(), test_samples_X.columns.tolist() + [\"isFraud\"]\n",
        "    )\n",
+        "    .set_custom_predict_fn(endpoint_predict_sample)\n",
+        "    .set_target_feature(TARGET_FEATURE)\n",
+        "    .set_label_vocab(LABEL_VOCAB)\n",
+        ")\n",
        "\n",
-        "    # Configure the WIT with the prediction function\n",
-        "    config_builder = (\n",
-        "        WitConfigBuilder(\n",
-        "            test_examples.tolist(), test_samples_X.columns.tolist() + [\"isFraud\"]\n",
-        "        )\n",
-        "        .set_custom_predict_fn(endpoint_predict_sample)\n",
-        "        .set_target_feature(TARGET_FEATURE)\n",
-        "        .set_label_vocab(LABEL_VOCAB)\n",
-        "    )\n",
-        "\n",
-        "    # run the WIT-widget\n",
-        "    display(WitWidget(config_builder, height=400))"
+        "# run the WIT-widget\n",
+        "WitWidget(config_builder, height=400)"
      ]
    },
    {
Author	SHA1	Message	Date
Andrew FerlitschandGitHub	44012a8516	Merge branch 'main' into model_monitoring_setup	2022-08-25 08:22:05 -07:00
Andrew Ferlitsch	479b375f4a	fix: review comments	2022-08-25 15:21:03 +00:00
Andrew Ferlitsch	060ab84e33	fix: review comments	2022-08-25 15:18:55 +00:00
Andrew Ferlitsch	49798b1949	Merge branch 'model_monitoring_setup' of https://github.com/GoogleCloudPlatform/vertex-ai-samples into model_monitoring_setup	2022-08-25 15:08:56 +00:00
Andrew Ferlitsch	61786a4b89	fix: review comments	2022-08-25 15:08:07 +00:00
Andrew Ferlitsch	6190eee61b	fix: review comments	2022-08-25 15:07:12 +00:00
Andrew FerlitschandGitHub	bdf1cf1a2e	Merge branch 'main' into model_monitoring_setup	2022-08-24 21:56:02 -07:00
Andrew FerlitschandGitHub	41f9548e46	Merge branch 'main' into model_monitoring_setup	2022-08-24 16:19:25 -07:00
Andrew Ferlitsch	c7527fef2a	Merge branch 'model_monitoring_setup' of https://github.com/GoogleCloudPlatform/vertex-ai-samples into model_monitoring_setup	2022-08-24 23:15:27 +00:00
Andrew Ferlitsch	3001067d1e	fix: fine-tuning	2022-08-24 23:14:24 +00:00
Andrew Ferlitsch	79470f1747	fix: fine-tuning	2022-08-24 23:12:51 +00:00
Andrew FerlitschandGitHub	7ad550d0c7	Merge branch 'main' into model_monitoring_setup	2022-08-24 08:54:16 -07:00
Andrew Ferlitsch	ff1c7f013b	feat: new mm notebook	2022-08-24 05:16:55 +00:00
Andrew Ferlitsch	10f1d368d2	feat: new mm notebook	2022-08-24 05:16:17 +00:00