Merge branch 'main' into batch_automl_tabular

feat: notebook for AutoML tabular batch prediction
2026-09-26 22:51:56 +00:00 · 2022-09-06 15:43:04 -04:00 · 2022-09-06 17:25:11 +00:00 · 2022-09-06 17:23:19 +00:00 · 2022-09-06 11:45:04 -04:00 · 2022-09-05 10:42:25 -07:00
25 changed files with 12946 additions and 364 deletions
@@ -1,5 +1,6 @@
 *  @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
@@ -0,0 +1,30 @@
+# 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 |
@@ -0,0 +1,91 @@
+
+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
@@ -0,0 +1,5 @@
+
+{
+    "0": "Negative", 
+    "1": "Positive"
+}
@@ -658,8 +658,8 @@
      },
      "outputs": [],
      "source": [
-        "datasets = load_dataset(\"imdb\")\n",
-        "datasets"
+        "dataset = load_dataset(\"imdb\")\n",
+        "dataset"
      ]
    },
    {
@@ -668,7 +668,7 @@
        "id": "RzfPtOMoIrIu"
      },
      "source": [
-        "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."
+        "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."
      ]
    },
    {
@@ -681,12 +681,12 @@
      "source": [
        "print(\n",
        "    \"Total # of rows in training dataset {} and size {:5.2f} MB\".format(\n",
-        "        datasets[\"train\"].shape[0], datasets[\"train\"].size_in_bytes / (1024 * 1024)\n",
+        "        dataset[\"train\"].shape[0], dataset[\"train\"].size_in_bytes / (1024 * 1024)\n",
        "    )\n",
        ")\n",
        "print(\n",
        "    \"Total # of rows in test dataset {} and size {:5.2f} MB\".format(\n",
-        "        datasets[\"test\"].shape[0], datasets[\"test\"].size_in_bytes / (1024 * 1024)\n",
+        "        dataset[\"test\"].shape[0], dataset[\"test\"].size_in_bytes / (1024 * 1024)\n",
        "    )\n",
        ")"
      ]
@@ -708,7 +708,7 @@
      },
      "outputs": [],
      "source": [
-        "datasets[\"train\"][0]"
+        "dataset[\"train\"][0]"
      ]
    },
    {
@@ -728,7 +728,7 @@
      },
      "outputs": [],
      "source": [
-        "label_list = datasets[\"train\"].unique(\"label\")\n",
+        "label_list = dataset[\"train\"].unique(\"label\")\n",
        "label_list"
      ]
    },
@@ -779,7 +779,7 @@
      },
      "outputs": [],
      "source": [
-        "show_random_elements(datasets[\"train\"])"
+        "show_random_elements(dataset[\"train\"])"
      ]
    },
    {
@@ -883,7 +883,7 @@
      },
      "outputs": [],
      "source": [
-        "example = datasets[\"train\"][4]\n",
+        "example = dataset[\"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",
-        "datasets = load_dataset(\"imdb\")\n",
+        "dataset = 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",
-        "datasets = datasets.map(preprocess_function, batched=True, load_from_cache_file=True)"
+        "dataset = dataset.map(preprocess_function, batched=True, load_from_cache_file=True)"
      ]
    },
    {
@@ -1091,8 +1091,8 @@
        "trainer = Trainer(\n",
        "    model,\n",
        "    args,\n",
-        "    train_dataset=datasets[\"train\"],\n",
-        "    eval_dataset=datasets[\"test\"],\n",
+        "    train_dataset=dataset[\"train\"],\n",
+        "    eval_dataset=dataset[\"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. Skip this step.\n",
+        "**If you are using Vertex AI Workbench Notebooks**, your environment is already authenticated. \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",
@@ -376,12 +376,11 @@
        "import sys\n",
        "\n",
        "# If on Vertex AI Workbench, then don't execute this code\n",
-        "IS_COLAB = False\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 \"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",
@@ -428,8 +427,9 @@
      },
      "outputs": [],
      "source": [
-        "if BUCKET_URI == \"\" or BUCKET_URI is None or BUCKET_URI == \"gs://[your-bucket-name]\":\n",
-        "    BUCKET_URI = \"gs://\" + PROJECT_ID + \"aip-\" + TIMESTAMP"
+        "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}\""
      ]
    },
    {
@@ -785,7 +785,7 @@
      },
      "outputs": [],
      "source": [
-        "endpoint.gca_resource"
+        "print(endpoint.gca_resource)"
      ]
    },
    {
@@ -908,7 +908,7 @@
      },
      "outputs": [],
      "source": [
-        "endpoint.gca_resource.deployed_models[0]"
+        "print(endpoint.gca_resource.deployed_models[0])"
      ]
    },
    {
@@ -1203,12 +1203,10 @@
        "\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 and resource URI for the existing `Model` resource.\n",
-        "- Use the `importer_node()` component to create a `VertexModel` pipeline artifact for the model.\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",
        "- Create an `Endpoint` resource.\n",
-        "- 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"
+        "- Using the `VertexModel` pipeline artifact, deploy the `Model` resource to the `Endpoint` resource."
      ]
    },
    {
@@ -1225,20 +1223,6 @@
        "\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",
@@ -1246,34 +1230,16 @@
        ")\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.types import artifact_types\n",
+        "    from google_cloud_pipeline_components.experimental.evaluation import \\\n",
+        "        GetVertexModelOp\n",
        "    from google_cloud_pipeline_components.v1.endpoint import (EndpointCreateOp,\n",
        "                                                              ModelDeployOp)\n",
-        "    from kfp.v2.components import importer_node\n",
        "\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",
+        "    model = GetVertexModelOp(model_resource_name=resource_name)\n",
        "\n",
        "    endpoint_op = EndpointCreateOp(\n",
        "        project=project,\n",
@@ -1281,7 +1247,7 @@
        "        display_name=display_name,\n",
        "    )\n",
        "\n",
-        "    deploy_op = ModelDeployOp(\n",
+        "    _ = ModelDeployOp(\n",
        "        model=model.outputs[\"model\"],\n",
        "        endpoint=endpoint_op.outputs[\"endpoint\"],\n",
        "        dedicated_resources_min_replica_count=1,\n",
@@ -1310,7 +1276,6 @@
        "\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."
      ]
@@ -1323,10 +1288,6 @@
      },
      "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",
@@ -1335,11 +1296,6 @@
        "        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",
@@ -1488,7 +1444,7 @@
        "\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 `importer_node()` component to create a `VertexEndpoint` pipeline artifact for the endpoint.\n",
+        "- Use the `GetVertexModelOp()` component to create a `VertexModel` pipeline artifact for the model.\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"
@@ -1504,6 +1460,7 @@
      "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",
@@ -1520,35 +1477,23 @@
        ")\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.types import artifact_types\n",
+        "    from google_cloud_pipeline_components.experimental.evaluation import \\\n",
+        "        GetVertexModelOp\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",
-        "    model = importer_node.importer(\n",
-        "        artifact_uri=resource_uri,\n",
-        "        artifact_class=artifact_types.VertexModel,\n",
-        "        metadata={\"resourceName\": resource_name},\n",
-        "    )\n",
+        "    from kfp.v2.components import importer_node\n",
+        "    from google_cloud_pipeline_components.types import artifact_types\n",
        "    \"\"\"\n",
        "\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",
+        "    model = GetVertexModelOp(model_resource_name=model_resource_name)\n",
        "\n",
        "    # Desired sequence: blocked by b/219835305\n",
        "    \"\"\"\n",
@@ -1562,7 +1507,7 @@
        "    # (WORKAROUND  b/219835305)\n",
        "    endpoint = return_unmanaged_endpoint(resource_name=endpoint_resource_name)\n",
        "\n",
-        "    deploy_op = ModelDeployOp(\n",
+        "    _ = ModelDeployOp(\n",
        "        model=model.outputs[\"model\"],\n",
        "        endpoint=endpoint.outputs[\"endpoint\"],\n",
        "        dedicated_resources_min_replica_count=1,\n",
@@ -1591,7 +1536,6 @@
        "\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",
@@ -1614,14 +1558,9 @@
        "        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",
@@ -385,12 +385,11 @@
        "import sys\n",
        "\n",
        "# If on Vertex AI Workbench, then don't execute this code\n",
-        "IS_COLAB = False\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 \"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",
@@ -1068,7 +1067,6 @@
        "- `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."
      ]
@@ -1085,7 +1083,6 @@
        "    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)"
@@ -1187,62 +1184,6 @@
        "    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": {
@@ -1251,7 +1192,7 @@
      "source": [
        "### Make the prediction request using SDK\n",
        "\n",
-        "Finally, use the `Vertex AI SDK` to make a prediction request."
+        "Next, 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:latest-gpu\"\n",
+        "    TF_IMAGE = \"tensorflow/serving:2.5.4-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:latest\"\n",
+        "    TF_IMAGE = \"tensorflow/serving:2.5.4\"\n",
        "\n",
        "if not IS_COLAB:\n",
        "    if DEPLOY_GPU:\n",
-        "        ! sudo docker pull tensorflow/serving:latest-gpu\n",
+        "        ! sudo docker pull tensorflow/serving:2.5.4-gpu\n",
        "    else:\n",
-        "        ! sudo docker pull tensorflow/serving:latest\n",
+        "        ! sudo docker pull tensorflow/serving:2.5.4\n",
        "\n",
-        "    ! docker tag tensorflow/serving $DEPLOY_IMAGE\n",
+        "    ! docker tag $TF_IMAGE $DEPLOY_IMAGE\n",
        "    ! docker push $DEPLOY_IMAGE\n",
        "else:\n",
        "    # install docker daemon\n",
@@ -1434,7 +1434,7 @@
      },
      "outputs": [],
      "source": [
-        "delete_bucket = False\n",
+        "delete_bucket = True\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 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",
+        "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",
        "\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 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",
+        "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",
        "\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",
@@ -176,7 +176,10 @@
        "else:\n",
        "    USER_FLAG = \"\"\n",
        "\n",
-        "! pip3 install --upgrade google-cloud-bigquery[pandas] google-cloud-aiplatform google-cloud-pipeline-components $USER_FLAG"
+        "! (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)"
      ]
    },
    {
@@ -8,7 +8,7 @@
      },
      "outputs": [],
      "source": [
-        "# Copyright 2022 Google LLC\n",
+        "# Copyright 2021 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",
@@ -66,17 +66,6 @@
        "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": {
@@ -101,6 +90,17 @@
        "* 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 google-cloud-storage $USER_FLAG"
+        "! pip3 install -U --upgrade tensorflow google-cloud-storage $USER_FLAG"
      ]
    },
    {
@@ -213,17 +213,6 @@
        "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": {
@@ -383,9 +372,9 @@
        "id": "timestamp"
      },
      "source": [
-        "#### Timestamp\n",
+        "#### 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 timestamp for each instance session, and append the timestamp 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 uuid for each instance session, and append it onto the name of resources you create in this tutorial."
      ]
    },
    {
@@ -396,9 +385,16 @@
      },
      "outputs": [],
      "source": [
-        "from datetime import datetime\n",
+        "import random\n",
+        "import string\n",
        "\n",
-        "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
+        "\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()"
      ]
    },
    {
@@ -409,7 +405,7 @@
      "source": [
        "### Authenticate your Google Cloud account\n",
        "\n",
-        "**If you are using Google Cloud Notebooks**, your environment is already authenticated. Skip this step.\n",
+        "**If you are using Google Cloud Notebooks**, your environment is already authenticated.\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",
@@ -494,7 +490,7 @@
      "outputs": [],
      "source": [
        "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"[your-bucket-name]\":\n",
-        "    BUCKET_URI = \"gs://\" + PROJECT_ID + \"aip-\" + TIMESTAMP"
+        "    BUCKET_URI = \"gs://\" + PROJECT_ID + \"aip-\" + UUID"
      ]
    },
    {
@@ -669,7 +665,7 @@
      "outputs": [],
      "source": [
        "dataset = aiplatform.ImageDataset.create(\n",
-        "    display_name=\"Salads\" + \"_\" + TIMESTAMP,\n",
+        "    display_name=\"Salads\" + \"_\" + UUID,\n",
        "    gcs_source=[IMPORT_FILE],\n",
        "    import_schema_uri=aiplatform.schema.dataset.ioformat.image.bounding_box,\n",
        ")\n",
@@ -717,7 +713,7 @@
      "outputs": [],
      "source": [
        "job = aiplatform.AutoMLImageTrainingJob(\n",
-        "    display_name=\"salads_\" + TIMESTAMP,\n",
+        "    display_name=\"salads_\" + UUID,\n",
        "    prediction_type=\"object_detection\",\n",
        "    multi_label=False,\n",
        "    model_type=\"CLOUD\",\n",
@@ -760,7 +756,7 @@
      "source": [
        "model = job.run(\n",
        "    dataset=dataset,\n",
-        "    model_display_name=\"salads_\" + TIMESTAMP,\n",
+        "    model_display_name=\"salads_\" + UUID,\n",
        "    training_fraction_split=0.8,\n",
        "    validation_fraction_split=0.1,\n",
        "    test_fraction_split=0.1,\n",
@@ -790,7 +786,7 @@
      "outputs": [],
      "source": [
        "# Get model resource ID\n",
-        "models = aiplatform.Model.list(filter=\"display_name=salads_\" + TIMESTAMP)\n",
+        "models = aiplatform.Model.list(filter=\"display_name=salads_\" + UUID)\n",
        "\n",
        "# Get a reference to the Model Service client\n",
        "client_options = {\"api_endpoint\": f\"{REGION}-aiplatform.googleapis.com\"}\n",
@@ -961,7 +957,7 @@
      "outputs": [],
      "source": [
        "batch_predict_job = model.batch_predict(\n",
-        "    job_display_name=\"salads_\" + TIMESTAMP,\n",
+        "    job_display_name=\"salads_\" + UUID,\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://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/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://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/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",
@@ -105,7 +105,7 @@
      "source": [
        "### Dataset\n",
        "\n",
-        "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."
+        "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."
      ]
    },
    {
@@ -29,6 +29,8 @@
        "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",
@@ -51,19 +53,22 @@
    {
      "cell_type": "markdown",
      "metadata": {
-        "id": "tvgnzT1CKxrO"
+        "id": "c4aaea3bab5e"
      },
      "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",
-        "\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",
+        "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": [
        "### 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",
@@ -79,8 +84,26 @@
        "- 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.\n",
+        "- Access imported features in offline jobs, such as training jobs."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "55e01a856f57"
+      },
+      "source": [
+        "### 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."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tvgnzT1CKxrO"
+      },
+      "source": [
        "### Costs \n",
        "\n",
        "This tutorial uses billable components of Google Cloud:\n",
@@ -262,7 +285,15 @@
      },
      "outputs": [],
      "source": [
-        "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}"
+        "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)"
      ]
    },
    {
@@ -275,10 +306,7 @@
      "source": [
        "if PROJECT_ID == \"\" or PROJECT_ID is None:\n",
        "    PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "    # 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)"
+        "print(\"Project ID: \", PROJECT_ID)"
      ]
    },
    {
@@ -320,7 +348,9 @@
      },
      "outputs": [],
      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
+        "REGION = \"[your-region]\"  # @param {type:\"string\"}\n",
+        "if REGION == \"[your-region]\":\n",
+        "    REGION = \"us-central1\""
      ]
    },
    {
@@ -329,9 +359,9 @@
        "id": "timestamp"
      },
      "source": [
-        "#### Timestamp\n",
+        "#### 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 timestamp for each instance session, and append the timestamp 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 uuid for each instance session, and append it onto the name of resources you create in this tutorial."
      ]
    },
    {
@@ -342,9 +372,16 @@
      },
      "outputs": [],
      "source": [
-        "from datetime import datetime\n",
+        "import random\n",
+        "import string\n",
        "\n",
-        "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
+        "\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()"
      ]
    },
    {
@@ -441,7 +478,7 @@
      "source": [
        "from google.cloud.aiplatform import Feature, Featurestore\n",
        "\n",
-        "FEATURESTORE_ID = \"movie_prediction\"\n",
+        "FEATURESTORE_ID = \"movie_prediction\" + UUID\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](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."
+        "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."
      ]
    },
    {
@@ -32,18 +32,26 @@
        "# 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/ai-platform-samples/blob/master/vertex-ai-samples/tree/master/notebooks/official/migration/UJ5%20Vertex%20SDK%20AutoML%20Image%20Object%20Detection.ipynb\">\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",
        "      <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/ai-platform-samples/blob/master/vertex-ai-samples/tree/master/notebooks/official/migration/UJ5%20Vertex%20SDK%20AutoML%20Image%20Object%20Detection.ipynb\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ5 Vertex SDK AutoML Image Object Detection.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/>"
      ]
    },
@@ -119,7 +127,7 @@
      "source": [
        "## Installation\n",
        "\n",
-        "Install the latest version of Vertex SDK for Python."
+        "Install the latest version of Vertex AI SDK for Python."
      ]
    },
    {
@@ -138,7 +146,7 @@
        "else:\n",
        "    USER_FLAG = \"\"\n",
        "\n",
-        "! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG"
+        "! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG -q"
      ]
    },
    {
@@ -150,17 +158,6 @@
        "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,
@@ -169,8 +166,9 @@
      },
      "outputs": [],
      "source": [
-        "if os.getenv(\"IS_TESTING\"):\n",
-        "    ! pip3 install --upgrade tensorflow $USER_FLAG"
+        "! pip3 install -U google-cloud-storage $USER_FLAG -q\n",
+        "\n",
+        "! pip3 install --upgrade tensorflow $USER_FLAG -q"
      ]
    },
    {
@@ -297,7 +295,10 @@
      },
      "outputs": [],
      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
+        "REGION = \"[your-region]\"  # @param {type: \"string\"}\n",
+        "\n",
+        "if REGION == \"[your-region]\":\n",
+        "    REGION = \"us-central1\""
      ]
    },
    {
@@ -306,9 +307,9 @@
        "id": "timestamp"
      },
      "source": [
-        "#### Timestamp\n",
+        "#### 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 timestamp for each instance session, and append the timestamp 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 uuid for each instance session, and append it onto the name of resources you create in this tutorial."
      ]
    },
    {
@@ -319,9 +320,16 @@
      },
      "outputs": [],
      "source": [
-        "from datetime import datetime\n",
+        "import random\n",
+        "import string\n",
        "\n",
-        "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
+        "\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()"
      ]
    },
    {
@@ -332,7 +340,7 @@
      "source": [
        "### Authenticate your Google Cloud account\n",
        "\n",
-        "**If you are using Google Cloud Notebooks**, your environment is already authenticated. Skip this step.\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",
@@ -367,8 +375,11 @@
        "import os\n",
        "import sys\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 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 \"google.colab\" in sys.modules:\n",
        "        from google.colab import auth as google_auth\n",
        "\n",
@@ -404,7 +415,8 @@
      },
      "outputs": [],
      "source": [
-        "BUCKET_NAME = \"gs://[your-bucket-name]\"  # @param {type:\"string\"}"
+        "BUCKET_NAME = \"[your-bucket-name]\"  # @param {type:\"string\"}\n",
+        "BUCKET_URI = f\"gs://{BUCKET_NAME}\""
      ]
    },
    {
@@ -415,8 +427,9 @@
      },
      "outputs": [],
      "source": [
-        "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"gs://[your-bucket-name]\":\n",
-        "    BUCKET_NAME = \"gs://\" + PROJECT_ID + \"aip-\" + TIMESTAMP"
+        "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}\""
      ]
    },
    {
@@ -436,7 +449,7 @@
      },
      "outputs": [],
      "source": [
-        "! gsutil mb -l $REGION $BUCKET_NAME"
+        "! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_URI"
      ]
    },
    {
@@ -456,7 +469,7 @@
      },
      "outputs": [],
      "source": [
-        "! gsutil ls -al $BUCKET_NAME"
+        "! gsutil ls -al $BUCKET_URI"
      ]
    },
    {
@@ -501,7 +514,7 @@
      },
      "outputs": [],
      "source": [
-        "aip.init(project=PROJECT_ID, staging_bucket=BUCKET_NAME)"
+        "aip.init(project=PROJECT_ID, staging_bucket=BUCKET_URI)"
      ]
    },
    {
@@ -603,7 +616,7 @@
      "outputs": [],
      "source": [
        "dataset = aip.ImageDataset.create(\n",
-        "    display_name=\"Salads\" + \"_\" + TIMESTAMP,\n",
+        "    display_name=\"Salads\" + \"_\" + UUID,\n",
        "    gcs_source=[IMPORT_FILE],\n",
        "    import_schema_uri=aip.schema.dataset.ioformat.image.bounding_box,\n",
        ")\n",
@@ -688,7 +701,7 @@
      "outputs": [],
      "source": [
        "dag = aip.AutoMLImageTrainingJob(\n",
-        "    display_name=\"salads_\" + TIMESTAMP,\n",
+        "    display_name=\"salads_\" + UUID,\n",
        "    prediction_type=\"object_detection\",\n",
        "    multi_label=False,\n",
        "    model_type=\"CLOUD\",\n",
@@ -742,7 +755,7 @@
      "source": [
        "model = dag.run(\n",
        "    dataset=dataset,\n",
-        "    model_display_name=\"salads_\" + TIMESTAMP,\n",
+        "    model_display_name=\"salads_\" + UUID,\n",
        "    training_fraction_split=0.8,\n",
        "    validation_fraction_split=0.1,\n",
        "    test_fraction_split=0.1,\n",
@@ -815,7 +828,7 @@
      "outputs": [],
      "source": [
        "# Get model resource ID\n",
-        "models = aip.Model.list(filter=\"display_name=salads_\" + TIMESTAMP)\n",
+        "models = aip.Model.list(filter=\"display_name=salads_\" + UUID)\n",
        "\n",
        "# Get a reference to the Model Service client\n",
        "client_options = {\"api_endpoint\": f\"{REGION}-aiplatform.googleapis.com\"}\n",
@@ -945,11 +958,11 @@
        "file_1 = test_item_1.split(\"/\")[-1]\n",
        "file_2 = test_item_2.split(\"/\")[-1]\n",
        "\n",
-        "! gsutil cp $test_item_1 $BUCKET_NAME/$file_1\n",
-        "! gsutil cp $test_item_2 $BUCKET_NAME/$file_2\n",
+        "! gsutil cp $test_item_1 $BUCKET_URI/$file_1\n",
+        "! gsutil cp $test_item_2 $BUCKET_URI/$file_2\n",
        "\n",
-        "test_item_1 = BUCKET_NAME + \"/\" + file_1\n",
-        "test_item_2 = BUCKET_NAME + \"/\" + file_2"
+        "test_item_1 = BUCKET_URI + \"/\" + file_1\n",
+        "test_item_2 = BUCKET_URI + \"/\" + file_2"
      ]
    },
    {
@@ -982,7 +995,7 @@
        "\n",
        "import tensorflow as tf\n",
        "\n",
-        "gcs_input_uri = BUCKET_NAME + \"/test.jsonl\"\n",
+        "gcs_input_uri = BUCKET_URI + \"/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",
@@ -1018,9 +1031,9 @@
      "outputs": [],
      "source": [
        "batch_predict_job = model.batch_predict(\n",
-        "    job_display_name=\"salads_\" + TIMESTAMP,\n",
+        "    job_display_name=\"salads_\" + UUID,\n",
        "    gcs_source=gcs_input_uri,\n",
-        "    gcs_destination_prefix=BUCKET_NAME,\n",
+        "    gcs_destination_prefix=BUCKET_URI,\n",
        "    sync=False,\n",
        ")\n",
        "\n",
@@ -1378,60 +1391,25 @@
      },
      "outputs": [],
      "source": [
-        "delete_all = True\n",
+        "# Delete the dataset using the Vertex dataset object\n",
        "\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",
+        "dataset.delete()\n",
        "\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",
+        "# Delete the model using the Vertex model object\n",
+        "model.delete()\n",
        "\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",
+        "# Delete the endpoint using the Vertex endpoint object\n",
+        "endpoint.delete()\n",
        "\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",
+        "# Delete the AutoML or Pipeline trainig job\n",
        "\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",
+        "dag.delete()\n",
        "\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",
+        "# Delete the batch prediction job using the Vertex batch prediction object\n",
+        "batch_predict_job.delete()\n",
        "\n",
-        "    # 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"
+        "if \"BUCKET_NAME\" in globals():\n",
+        "    ! gsutil rm -r $BUCKET_URI"
      ]
    }
  ],
@@ -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==1.0.1 --quiet --no-warn-conflicts"
+        "                                    google-cloud-pipeline-components==1.0.18 --quiet --no-warn-conflicts"
      ]
    },
    {
@@ -733,9 +733,7 @@
        "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)"
@@ -763,14 +761,14 @@
        "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 = \"spark\"\n",
-        "TASK = \"classifier\"\n",
+        "ML_APPLICATION = \"loan-eligibility\"\n",
+        "TASK = \"sparkml\"\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",
@@ -785,7 +783,6 @@
        "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",
@@ -800,10 +797,9 @@
        "\n",
        "# Condition\n",
        "AUPR_THRESHOLD = 0.5\n",
-        "AUPR_HYPERTUNE_CONDITION = \"[AUPR_HYPERTUNE]\"\n",
+        "AUPR_HYPERTUNE_CONDITION = \"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",
@@ -814,7 +810,24 @@
        "    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\""
      ]
    },
    {
@@ -843,7 +856,7 @@
        "id": "LB2aM7VyRyZG"
      },
      "source": [
-        "## PART I - Build the Vertex Pipeline to train and deploy a Spark model\n",
+        "## 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",
@@ -851,10 +864,16 @@
        "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.   Register the model in the Vertex AI Model Registry\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"
      ]
    },
    {
@@ -1430,7 +1449,9 @@
        "\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."
+        "- `--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."
      ]
    },
    {
@@ -1467,6 +1488,9 @@
        "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",
@@ -1572,6 +1596,16 @@
        "        ''',\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",
@@ -1728,6 +1762,7 @@
        "    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",
@@ -1759,10 +1794,20 @@
        "        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",
@@ -1807,11 +1852,11 @@
        "id": "68nYBB5GS9TB"
      },
      "source": [
-        "### Build a custom dataproc serverless image\n",
+        "### Build a custom Dataproc Serverless container image\n",
        "\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",
+        "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",
        "\n",
-        "**Note:** This step is optional and is included here for general awareness."
+        "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."
      ]
    },
    {
@@ -1820,7 +1865,7 @@
        "id": "GF9_5IGYqLAX"
      },
      "source": [
-        "#### Define the Dataproc serverless custom runtime image"
+        "#### Define the Dataproc Serverless custom runtime image"
      ]
    },
    {
@@ -1891,7 +1936,8 @@
        "      python \\\n",
        "      scikit-image \\\n",
        "      scikit-learn \\\n",
-        "      scipy  \n",
+        "      scipy \\\n",
+        "      mleap\n",
        "\n",
        "# (Required) Create the 'spark' group/user.\n",
        "# The GID and UID must be 1099. Home directory is required.\n",
@@ -1927,7 +1973,9 @@
        "id": "ZXzI2xInqb3V"
      },
      "source": [
-        "#### Build the Dataproc serverless custom runtime using Google Cloud Build"
+        "#### Build the Dataproc Serverless custom runtime using Google Cloud Build\n",
+        "\n",
+        "**Note:** this step may take approximately 5 to 10 minutes to complete."
      ]
    },
    {
@@ -2104,12 +2152,12 @@
    {
      "cell_type": "markdown",
      "metadata": {
-        "id": "1-Ccx4uLDz4N"
+        "id": "28f3d22dd97f"
      },
      "source": [
-        "#### Model registration custom component\n",
+        "#### Create component for passing args to hyperparameter tuning component\n",
        "\n",
-        "Define a component to create a model resource for the trained model on Vertex AI Model registry."
+        "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."
      ]
    },
    {
@@ -2120,22 +2168,230 @@
      },
      "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 register_model(\n",
-        "    artifact_uri: str,\n",
-        "    model: Output[Artifact],\n",
-        ") -> NamedTuple(\"Outputs\", [(\"uri\", str)]):\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",
        "\n",
-        "    component_outputs = NamedTuple(\n",
-        "        \"Outputs\",\n",
-        "        [\n",
-        "            (\"uri\", str),\n",
-        "        ],\n",
-        "    )\n",
-        "    return component_outputs(artifact_uri)"
+        "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"
      ]
    },
    {
@@ -2159,30 +2415,35 @@
      "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",
-        "\n",
-        "    from google_cloud_pipeline_components.experimental.dataproc import \\\n",
+        "    from google_cloud_pipeline_components.v1.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",
@@ -2194,13 +2455,12 @@
        "        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 = vertex_ai_components.TabularDatasetCreateOp(\n",
+        "    create_dataset_op = TabularDatasetCreateOp(\n",
        "        display_name=dataset_name,\n",
        "        gcs_source=dataset_uri,\n",
        "        project=project_id,\n",
@@ -2220,7 +2480,6 @@
        "        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",
@@ -2233,11 +2492,12 @@
        "        name=AUPR_HYPERTUNE_CONDITION,\n",
        "    ):\n",
        "\n",
-        "        build_hpt_args_op = build_training_args(\n",
+        "        build_hpt_args_op = build_hpt_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",
@@ -2245,13 +2505,46 @@
        "            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",
-        "        # upload model\n",
-        "        register_model(artifact_uri=hpt_model_path).after(hyperparameter_tuning_op)"
+        "        # 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)"
      ]
    },
    {
@@ -2327,6 +2620,65 @@
        "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": {
@@ -2352,8 +2704,14 @@
        "# 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.TabularDataset.list(filter=f'display_name=\"{MODEL_NAME}\"')\n",
+        "model_list = vertex_ai.Model.list(filter=f'display_name=\"{MODEL_NAME}\"')\n",
        "for model in model_list:\n",
        "    model.delete()\n",
        "\n",
Author	SHA1	Message	Date
gericdongandGitHub	9b3e2391f3	Merge branch 'main' into batch_automl_tabular	2022-09-06 15:43:04 -04:00
Andrew Ferlitsch	8d0188e59a	feat: notebook for AutoML tabular batch prediction	2022-09-06 17:25:11 +00:00
Andrew Ferlitsch	2b0343a52f	feat: notebook for AutoML tabular batch prediction	2022-09-06 17:23:19 +00:00
Andrew FerlitschandGitHub	14b2ce4f2e	feat: notebook for batch predict for automl image models (#927 ) * feat: batch predict for automl image model * feat: batch predict for automl image model	2022-09-06 11:45:04 -04:00
Chun-Hsiang WangandGitHub	c14b98c92d	samples: Minor fix for the wording. (#924 )	2022-09-05 10:42:25 -07:00
gericdong GitHub Andrew Ferlitsch	8275ea6c49	fix: correct the download_url (#923 ) * fix: correct the download_url * fix: fixed formatting issue Co-authored-by: Andrew Ferlitsch <aferlitsch@google.com>	2022-09-02 17:04:42 -04:00
uday kumar GitHub Andrew Ferlitsch	40fbffcc95	Sdk automl image object detection batch (#869 ) * changed to andrew comments * changes according to andrew comments * changes according to andrew comments * review changes * review changes * review changes Co-authored-by: Andrew Ferlitsch <aferlitsch@google.com>	2022-09-02 13:34:25 -07:00
Andrew FerlitschandGitHub	08bb513488	feat: Batch prediction for custom tabular model (#920 ) * feat: add notebook for custom tabular batch predict * feat: add notebook for custom tabular batch predict	2022-09-01 20:33:34 -04:00
Chun-Hsiang WangandGitHub	b298f83cd3	Vertex Prediction PyTorch Experimental: Add a sample for pre-built PyTorch deployments. (#898 ) * samples: Add a new sample for pre-built Pytorch deployments. It's borrowed from the examples in community-content/pytorch_text_classification_using_vertex_sdk_and_gcloud. * samples: Removed all training related stuff in the notebooks. * samples: Fixed comments. * samples: Updated readme. * samples: Updated emails for Pytorch launch.	2022-09-01 11:48:59 -07:00
Andrew FerlitschandGitHub	659cbb54c4	feat: add model monitoring with custom container (#919 ) * feat: add notebook using custom deployment container * feat: add notebook using custom deployment container	2022-09-01 10:22:05 -07:00
Andrew Ferlitsch GitHub gericdong	6ddcaa540a	fix: tf serving workaround (#917 ) * fix: pin TF serving image * fix: pin TF serving image Co-authored-by: gericdong <itseric@google.com>	2022-09-01 12:58:27 -04:00
Andrew Ferlitsch GitHub gericdong	1656c57b18	feat: extend image batch notebook (#916 ) * feat: add notebook for custom image model batch prediction * feat: add notebook for custom image model batch prediction * fix: review comments * fix: review comments * feat: extend image batch notebook * feat: extend image batch notebook Co-authored-by: gericdong <itseric@google.com>	2022-09-01 09:56:36 -07:00
SamyuktaDR GitHub Andrew Ferlitsch	6cac60f74a	Sdk feature store ver1 (#790 ) * Added condition to create Featurestore if it doesn't exist * Ran Linter Test * Made changes mentioned in review * Ran Linter Test * Attached uuid to featurestore_id to avoid error while creating featurestore with existing name * Ran linter test Co-authored-by: Andrew Ferlitsch <aferlitsch@google.com>	2022-09-01 08:57:16 -07:00
Andrew FerlitschandGitHub	55e37f795c	feat: notebook for batch prediction with custom image model (#915 ) * feat: add notebook for custom image model batch prediction * feat: add notebook for custom image model batch prediction * fix: review comments * fix: review comments	2022-08-31 10:38:31 -07:00
Andrew Maguire GitHub Andrew Ferlitsch	c030d7ef74	use `dataset` instead of `datasets` (#892 ) less chance for an error and confusion in name clashing with the `datasets` pypi package also used in the notebook. Co-authored-by: Andrew Ferlitsch <aferlitsch@google.com>	2022-08-31 07:44:46 -07:00
Andrew FerlitschandGitHub	96be449c69	feat: add notebook for MM autoML (#912 ) * feat: model monitoring for AutoML * feat: model monitoring for AutoML * fix: correction on AutoML * fix: correction on AutoML	2022-08-30 12:45:45 -07:00
wintwoo GitHub Win Woo Andrew Ferlitsch	e40ddab4d5	Upgrade to DataprocPySparkBatch v1 component and add Vertex AI placeholder features (#910 ) * Fix typo in notebook heading. * Fix linting issues. * Use gcpc v1 components and use Vertex AI for model upload and serving. * Notebook cleanup * Minor heading cleanup * Fix linting issues * Fix linting issues * Fix linting issues * Fix linting issues Co-authored-by: Win Woo <wwoo@google.com> Co-authored-by: Andrew Ferlitsch <aferlitsch@google.com>	2022-08-30 10:46:21 -07:00
Michael HuandGitHub	d02bc2d56b	pin arima notebook dependencies (#905 ) Pins the versions of packages installed in the notebook in anticipation for a breaking change to the GCPC package.	2022-08-29 19:35:54 -04:00
Andrew Ferlitsch GitHub nayaknishant	76b641b23d	fix: private endpoint example (#909 ) * fix: remove gcloud usage * fix: remove gcloud usage * fix: review comments * fix: review comments Co-authored-by: nayaknishant <nishantnayak@google.com>	2022-08-29 12:16:18 -07:00
Andrew FerlitschandGitHub	bbf4345e76	fix: remove Pantheon links (#908 ) * fix: issue 194103604 * fix: issue 194103604	2022-08-28 11:24:32 -04:00
udaypunna GitHub Andrew Ferlitsch	058358a795	Vertex SDK AutoML Image Object Detection (#808 ) * new notebook Vertex SDK AutoML Image Object Detection * new notebook Vertex SDK AutoML Image Object Detection * new notebook of Vertex SDK AutoML Image Object Detection * new notebook of Vertex SDK AutoML Image Object Detection * linter test * linter test * andrew commented changes * andrew commented changes * review changes * review changes Co-authored-by: Andrew Ferlitsch <aferlitsch@google.com>	2022-08-26 15:56:46 -07:00
Andrew FerlitschandGitHub	1c2f75f680	update: GetVertexModelOp (#907 ) * update: use GetVertexModelOp * update: use GetVertexModelOp	2022-08-26 12:12:09 -07:00
Andrew FerlitschandGitHub	999866fad1	feat: model monitoring custom (#906 ) * feat: notebook for custom models * feat: notebook for custom models * fix: refining * fix: refining * fix: review updates * fix: review updates	2022-08-26 08:58:54 -07:00