fix, chore, refactor(egen): hardcodes scikit-learn version to 1.2, changes python version from 3.9 to 3.10, adds numpy==1.26.4 installation, adds code to undeploy model from endpoints (#3228)

* fix, chore, refactor(egen): hardcodes scikit-learn version to 1.2, changes python version from 3.9 to 3.10, adds numpy==1.26.4 installation, adds code to undeploy model from endpoints, rusage of future tense * wording changes * markdown wording changes as per PR comments
2026-09-26 14:42:04 +00:00 · 2024-07-10 21:51:01 +00:00
parent f22fee6f84
commit 0583f152ac
1 changed files with 172 additions and 155 deletions
@@ -32,24 +32,26 @@
        "# Vertex AI: Track artifacts and metrics across Vertex AI Pipelines runs using Vertex ML Metadata\n",
        "\n",
        "<table align=\"left\">\n",
-        "\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/ml_metadata/vertex-pipelines-ml-metadata.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
        "    </a>\n",
        "  </td>\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fml_metadata%2Fvertex-pipelines-ml-metadata.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
+        "    </a>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\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/ml_metadata/vertex-pipelines-ml-metadata.ipynb\">\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/ml_metadata/vertex-pipelines-ml-metadata.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> 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://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/ml_metadata/vertex-pipelines-ml-metadata.ipynb\" target='_blank'>\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>"
      ]
    },
@@ -61,7 +63,7 @@
      "source": [
        "## Overview\n",
        "\n",
-        "This notebook demonstrates how to track metrics and artifacts across Vertex AI Pipelines runs, and analyze this metadata using the Vertex AI SDK for Pyrhon. If you'd prefer to follow a step-by-step tutorial, check out the [codelab version](https://codelabs.developers.google.com/vertex-mlmd-pipelines#0) of this notebook.\n",
+        "This notebook demonstrates how to track metrics and artifacts across Vertex AI Pipeline runs, and analyze this metadata using the Vertex AI Python SDK. If you'd prefer to follow a step-by-step tutorial, check out the [codelab version](https://codelabs.developers.google.com/vertex-mlmd-pipelines#0) of this notebook.\n",
        "\n",
        "Learn more about [Vertex ML Metadata](https://cloud.google.com/vertex-ai/docs/ml-metadata) and [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction)."
      ]
@@ -86,7 +88,7 @@
        "* Use the Kubeflow Pipelines SDK to build an ML pipeline that runs on Vertex AI.\n",
        "* The pipeline creates a dataset, trains a scikit-learn model, and deploys the model to an endpoint.\n",
        "* Write custom pipeline components that generate artifacts and metadata.\n",
-        "* Compare Vertex AI Pipelines runs, both in the Google Cloud console and programmatically.\n",
+        "* Compare Vertex AI Pipeline runs, both in the Google Cloud console and programmatically.\n",
        "* Trace the lineage for pipeline-generated artifacts.\n",
        "* Query your pipeline run metadata."
      ]
@@ -99,7 +101,7 @@
      "source": [
        "### Dataset\n",
        "\n",
-        "In this notebook, you'll train a model using scikit-learn to classify bean types using the [Dry Beans Dataset](https://archive.ics.uci.edu/ml/datasets/Dry+Bean+Dataset) from UCI Machine Learning. This is a tabular dataset that includes measurements and characteristics of seven different types of beans taken from images."
+        "This notebook uses scikit-learn to train a model and classify bean types using the [Dry Beans Dataset](https://archive.ics.uci.edu/ml/datasets/Dry+Bean+Dataset) from UCI Machine Learning. This is a tabular dataset that includes measurements and characteristics of seven different types of beans taken from images."
      ]
    },
    {
@@ -127,12 +129,19 @@
    {
      "cell_type": "markdown",
      "metadata": {
-        "id": "i7EUnXsZhAGF"
+        "id": "61RBz8LLbxCR"
      },
      "source": [
-        "### Install additional packages\n",
-        "\n",
-        "Run the following commands to install the Vertex AI SDK for Python and packages used in this notebook."
+        "## Get started"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "No17Cw5hgx12"
+      },
+      "source": [
+        "### Install Vertex AI SDK for Python and other required packages\n"
      ]
    },
    {
@@ -150,137 +159,97 @@
    {
      "cell_type": "markdown",
      "metadata": {
-        "id": "restart"
+        "id": "R5Xep4W9lq-Z"
      },
      "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel"
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
-        "id": "D-ZBOjErv5mM"
+        "id": "XRvKdaPDTznN"
      },
      "outputs": [],
      "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
        "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
-        "id": "yfEglUHQk9S3"
+        "id": "SbmM4z7FOBpM"
      },
      "source": [
-        "## Before you begin\n",
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dmWOrTJ3gx13"
+      },
+      "source": [
+        "### Authenticate your notebook environment (Colab only)\n",
        "\n",
-        "### Set your project ID\n",
-        "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Authenticate your environment on Google Colab.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
-        "id": "set_project_id"
+        "id": "NyKGtVQjgx13"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DF4l8DTdWgPY"
+      },
+      "source": [
+        "### Set Google Cloud project information\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project. Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Nqwi-5ufWp_B"
      },
      "outputs": [],
      "source": [
        "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}\n",
        "\n",
        "# Set the project id\n",
        "! gcloud config set project {PROJECT_ID}"
      ]
    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gcp_authenticate"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ce6043da7b33"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0367eac06a10"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "21ad4dbb4a61"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
-      ]
-    },
    {
      "cell_type": "markdown",
      "metadata": {
@@ -344,7 +313,7 @@
      },
      "outputs": [],
      "source": [
-        "! gsutil mb -l $REGION $BUCKET_URI"
+        "! gsutil mb -l {LOCATION} -p {PROJECT_ID} {BUCKET_URI}"
      ]
    },
    {
@@ -491,7 +460,7 @@
        "id": "2937d462a96a"
      },
      "source": [
-        "Initialize the Vertex AI SDK"
+        "### Initialize Vertex AI SDK for Python"
      ]
    },
    {
@@ -502,7 +471,7 @@
      },
      "outputs": [],
      "source": [
-        "aiplatform.init(project=PROJECT_ID, location=REGION)"
+        "aiplatform.init(project=PROJECT_ID, location=LOCATION)"
      ]
    },
    {
@@ -523,7 +492,7 @@
      },
      "source": [
        "### Pipeline Run\n",
-        "The term “run” refers to a single execution of your pipeline in Vertex AI Pipelines. Each run generates artifacts, metrics, and associated metadata."
+        "The term “run” refers to a single execution of your pipeline in Vertex AI Pipelines, during which artifacts, metrics, and associated metadata are generated."
      ]
    },
    {
@@ -534,7 +503,7 @@
      "source": [
        "### Artifact\n",
        "\n",
-        "An artifact is a resource generated by your pipeline. Artifacts could datasets, models, endpoints, or custom resources defined in your pipeline."
+        "An artifact is a resource generated by your pipeline. Artifacts can be datasets, models, endpoints, or custom resources defined in your pipeline."
      ]
    },
    {
@@ -545,7 +514,7 @@
      "source": [
        "### Metric\n",
        "\n",
-        "A metric is a way to measure the performance of your pipeline runs and artifacts. For example, a metric could be the accuracy of a classification model artifact created in your pipeline, or the size of the dataset used to train your model."
+        "A metric is a way to measure the performance of your pipeline runs and artifacts. For example, a metric can be the accuracy of a classification model artifact created in your pipeline, or the size of the dataset used to train your model."
      ]
    },
    {
@@ -556,7 +525,7 @@
      "source": [
        "### Metadata\n",
        "\n",
-        "Metadata describes the artifacts and metrics generated by your pipeline runs. Metadata on a model, for example, could include the URL of the model artifacts, its name, and the time it was created."
+        "Metadata describes the artifacts and metrics generated by your pipeline runs. Metadata on a model, for example, includes the URL of the model artifacts, its name, and the time it was created."
      ]
    },
    {
@@ -567,7 +536,7 @@
      "source": [
        "## Creating a 3-step pipeline with custom components\n",
        "\n",
-        "The focus of this lab is on understanding metadata from pipeline runs. In order to do that, you'll need a pipeline to run on Vertex AI Pipelines, which is where you’ll start. Here you’ll define a 3-step pipeline with the following custom components:\n",
+        "The focus of this lab is on understanding metadata from pipeline runs. To do that, you need a pipeline to run on Vertex AI Pipelines, which is where you start. Here, you define a 3-step pipeline with the following custom components:\n",
        "\n",
        "* `get_dataframe`: Retrieve data from a BigQuery table and convert it into a pandas DataFrame.\n",
        "* `train_sklearn_model`: Use the pandas DataFrame to train and export a scikit-learn model, along with some metrics.\n",
@@ -604,14 +573,16 @@
      "outputs": [],
      "source": [
        "@component(\n",
-        "    packages_to_install=[\"google-cloud-bigquery\", \"pandas\", \"pyarrow\"],\n",
-        "    base_image=\"python:3.9\",\n",
+        "    packages_to_install=[\"google-cloud-bigquery[pandas]\", \"pyarrow\"],\n",
+        "    base_image=\"python:3.10\",\n",
        "    output_component_file=\"create_dataset.yaml\",\n",
        ")\n",
-        "def get_dataframe(bq_table: str, output_data_path: OutputPath(\"Dataset\")):\n",
+        "def get_dataframe(\n",
+        "    project_id: str, bq_table: str, output_data_path: OutputPath(\"Dataset\")\n",
+        "):\n",
        "    from google.cloud import bigquery\n",
        "\n",
-        "    bqclient = bigquery.Client(project=PROJECT_ID)\n",
+        "    bqclient = bigquery.Client(project=project_id)\n",
        "    table = bigquery.TableReference.from_string(bq_table)\n",
        "    rows = bqclient.list_rows(table)\n",
        "    dataframe = rows.to_dataframe(\n",
@@ -644,8 +615,8 @@
      "outputs": [],
      "source": [
        "@component(\n",
-        "    packages_to_install=[\"scikit-learn\", \"pandas\", \"joblib\"],\n",
-        "    base_image=\"python:3.9\",\n",
+        "    packages_to_install=[\"scikit-learn==1.2\", \"pandas\", \"joblib\", \"numpy==1.26.4\"],\n",
+        "    base_image=\"python:3.10\",\n",
        "    output_component_file=\"beans_model_component.yaml\",\n",
        ")\n",
        "def sklearn_train(\n",
@@ -691,7 +662,7 @@
      "source": [
        "@component(\n",
        "    packages_to_install=[\"google-cloud-aiplatform\"],\n",
-        "    base_image=\"python:3.9\",\n",
+        "    base_image=\"python:3.10\",\n",
        "    output_component_file=\"beans_deploy_component.yaml\",\n",
        ")\n",
        "def deploy_model(\n",
@@ -708,7 +679,7 @@
        "    deployed_model = aiplatform.Model.upload(\n",
        "        display_name=\"beans-model-pipeline\",\n",
        "        artifact_uri=model.uri.replace(\"model\", \"\"),\n",
-        "        serving_container_image_uri=\"us-docker.pkg.dev/vertex-ai/prediction/sklearn-cpu.0-24:latest\",\n",
+        "        serving_container_image_uri=\"us-docker.pkg.dev/vertex-ai/prediction/sklearn-cpu.1-2:latest\",\n",
        "    )\n",
        "    endpoint = deployed_model.deploy(machine_type=\"n1-standard-4\")\n",
        "\n",
@@ -741,12 +712,12 @@
        "    name=\"mlmd-pipeline\",\n",
        ")\n",
        "def pipeline(\n",
-        "    bq_table: str = \"\",\n",
-        "    output_data_path: str = \"data.csv\",\n",
-        "    project: str = PROJECT_ID,\n",
-        "    region: str = REGION,\n",
+        "    bq_table: str,\n",
+        "    output_data_path: str,\n",
+        "    project: str,\n",
+        "    region: str,\n",
        "):\n",
-        "    dataset_task = get_dataframe(bq_table)\n",
+        "    dataset_task = get_dataframe(project, bq_table)\n",
        "\n",
        "    model_task = sklearn_train(dataset_task.output)\n",
        "\n",
@@ -759,7 +730,7 @@
        "id": "910541af051c"
      },
      "source": [
-        "The following generates a JSON file that you'll use to run the pipeline:"
+        "The following generates a JSON file that is then used to run the pipeline:"
      ]
    },
    {
@@ -779,9 +750,22 @@
        "id": "u-iTnzt3B6Z_"
      },
      "source": [
-        "### Start two pipeline runs\n",
+        "### Initiate pipeline runs\n",
        "\n",
-        "Next you'll kick ofg **two** runs of our pipeline. First, define a timestamp to use for our pipeline job IDs:"
+        "First, define a timestamp to use as your pipeline job IDs.\n",
+        "\n",
+        "Then for each run, create an instance of `PipelineJob` from the `pipeline_jobs` module.\n",
+        " For each instance provide the following details:\n",
+        "\n",
+        "* `display_name` : Human-readable name for the pipeline job.\n",
+        "* `template_path` : This specifies the path to the pipeline template file in JSON format, which contains the pipeline's configuration and structure created in the previous steps.\n",
+        "* `job_id` : This sets a unique identifier for the job.\n",
+        "* `parameter_values` : This dictionary contains key-value pairs for the parameters required by the pipeline which are metioned during pipeline definition.\n",
+        "    * `bq_table` :  Specifies the BigQuery table to use.\n",
+        "    * `output_data_path` : Defines the path for the output data file.\n",
+        "    * `project` : Specifies the Google Cloud project ID.\n",
+        "    * `region` : Defines the region where the pipeline will run.\n",
+        "* enable_caching : When set to `True`, caching is enabled for the pipeline run. This lets the system reuse previous results, if the same job has been executed before with identical parameters, thereby saving time and resources"
      ]
    },
    {
@@ -803,7 +787,7 @@
        "id": "3d380ed72490"
      },
      "source": [
-        "The pipeline takes one parameter when you run it: the `bq_table` we want to use for training data you’ll use for training data. This pipeline run uses a smaller version of the beans dataset:"
+        "Create a pipeline run using the smaller version beans dataset."
      ]
    },
    {
@@ -818,7 +802,12 @@
        "    display_name=\"mlmd-pipeline\",\n",
        "    template_path=\"mlmd_pipeline.json\",\n",
        "    job_id=\"mlmd-pipeline-small-{}\".format(TIMESTAMP),\n",
-        "    parameter_values={\"bq_table\": \"sara-vertex-demos.beans_demo.small_dataset\"},\n",
+        "    parameter_values={\n",
+        "        \"bq_table\": \"sara-vertex-demos.beans_demo.small_dataset\",\n",
+        "        \"output_data_path\": \"data.csv\",\n",
+        "        \"project\": PROJECT_ID,\n",
+        "        \"region\": REGION,\n",
+        "    },\n",
        "    enable_caching=True,\n",
        ")"
      ]
@@ -844,7 +833,12 @@
        "    display_name=\"mlmd-pipeline\",\n",
        "    template_path=\"mlmd_pipeline.json\",\n",
        "    job_id=\"mlmd-pipeline-large-{}\".format(TIMESTAMP),\n",
-        "    parameter_values={\"bq_table\": \"sara-vertex-demos.beans_demo.large_dataset\"},\n",
+        "    parameter_values={\n",
+        "        \"bq_table\": \"sara-vertex-demos.beans_demo.large_dataset\",\n",
+        "        \"output_data_path\": \"data.csv\",\n",
+        "        \"project\": PROJECT_ID,\n",
+        "        \"region\": REGION,\n",
+        "    },\n",
        "    enable_caching=True,\n",
        ")"
      ]
@@ -895,7 +889,7 @@
        "id": "cc15017be48e"
      },
      "source": [
-        "After running this cell, you'll see a link to view each pipeline in the Google Cloud console. Open that link to see more details on your pipeline.\n",
+        "After running this cell, there is a link to view each pipeline in the Google Cloud console. Open that link to get more details about your pipeline.\n",
        "\n",
        "**These pipeline runs will take 10-15 minutes to complete.**"
      ]
@@ -915,9 +909,9 @@
        "id": "A1PqKxlpOZa2"
      },
      "source": [
-        "Now that you have two pipeline completed pipeline runs, you're ready to take a closer look at pipeline metrics using the Vertex AI SDK for Python.\n",
+        "Once both the pipelines run successfully, you're ready to take a closer look at pipeline metrics using the Vertex AI SDK for Python.\n",
        "\n",
-        "**For guidance on inspecting pipeline artifacts and metadata in the Google Cloud console, see [this codelab](https://codelabs.developers.google.com/vertex-mlmd-pipelines#5).**"
+        "For guidance on inspecting pipeline artifacts and metadata in the Google Cloud console, check out this codelab: [Understanding pipeline artifacts and lineage](https://codelabs.developers.google.com/vertex-mlmd-pipelines#5)\n"
      ]
    },
    {
@@ -926,7 +920,8 @@
        "id": "jbRf1WoH_vbY"
      },
      "source": [
-        "You can use the `aiplatform.get_pipeline_df()` method to access run metadata. Here, you'll get metadata for the last two runs of the same pipeline and load it into a Pandas DataFrame. The `mlmd-pipeline` parameter here refers to the name you gave your pipeline in the pipeline definition:"
+        "Use `aiplatform.get_pipeline_df()` method to retrieve the metadata for the last two runs of the pipeline. Then, load it into a Pandas DataFrame. \n",
+        "The `pipeline` parameter specifies the name of your pipeline as defined in the pipeline configuration, which in this case is *mlmd-pipeline*."
      ]
    },
    {
@@ -947,7 +942,7 @@
        "id": "d23e2cb66265"
      },
      "source": [
-        "You’ve only executed the pipeline twice here, but you can imagine how many metrics you'd have with more executions. Next, create a custom visualization with matplotlib to see the relationship between the model's accuracy and the amount of data used for training. Run the following to generate a graph:"
+        "You’ve only executed the pipeline twice here, but you can imagine how many metrics you'd have with more executions. Next, create a custom visualization with matplotlib to see the relationship between the model accuracy and the amount of data used for training. Run the following to generate a graph:"
      ]
    },
    {
@@ -979,7 +974,7 @@
        "id": "4431b5d062f3"
      },
      "source": [
-        "In addition to getting a DataFrame of all pipeline metrics, you may want to programmatically query artifacts created in your ML system. From there you can create a custom dashboard or let others in your organizaiton get details on specific artifacts."
+        "In addition to creating a DataFrame of all pipeline metrics, you can programmatically query artifacts created in your ML system. From there you can create a custom dashboard or let others in your organizaiton get details on specific artifacts."
      ]
    },
    {
@@ -990,7 +985,7 @@
      "source": [
        "### Getting all Model artifacts\n",
        "\n",
-        "To query artifacts in this way, you'll create a `MetadataServiceClient`:"
+        "To query artifacts in this way, create a `MetadataServiceClient`:"
      ]
    },
    {
@@ -1056,7 +1051,7 @@
        "id": "F19_5lw0MqXv"
      },
      "source": [
-        "Next, get all artifacts created after August 10, 2021 with a `LIVE` state. After you run this request, display the results in a pandas DataFrame. First, execute the request:"
+        "Next, get all artifacts created after August 10, 2021 that are in `LIVE` state. "
      ]
    },
    {
@@ -1122,7 +1117,9 @@
        "\n",
        "* The pipeline runs you executed deployed endpoints in Vertex AI. Navigate to the [Google Cloud console](https://console.cloud.google.com/vertex-ai/endpoints) to delete those endpoints.\n",
        "\n",
-        "* Delete the [Cloud Storage bucket](https://console.cloud.google.com/storage/browser/) you created."
+        "* Delete the [Cloud Storage bucket](https://console.cloud.google.com/storage/browser/) you created.\n",
+        "\n",
+        "Alternatively, you can execute the below cell to clean up the resources used in this notebook."
      ]
    },
    {
@@ -1133,16 +1130,36 @@
      },
      "outputs": [],
      "source": [
-        "import os\n",
-        "\n",
+        "# delete pipelines\n",
        "try:\n",
        "    run1.delete()\n",
        "    run2.delete()\n",
        "except Exception as e:\n",
        "    print(e)\n",
        "\n",
-        "delete_bucket = False\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
+        "# undeploy model from endpoints\n",
+        "endpoints = aiplatform.Endpoint.list(\n",
+        "    filter='display_name=\"beans-model-pipeline_endpoint\"'\n",
+        ")\n",
+        "for endpoint in endpoints:\n",
+        "    deployed_models = endpoint.list_models()\n",
+        "    for deployed_model in deployed_models:\n",
+        "        endpoint.undeploy(deployed_model_id=deployed_model.id)\n",
+        "    # delete endpoint\n",
+        "    endpoint.delete()\n",
+        "\n",
+        "# delete model\n",
+        "model_ids = aiplatform.Model.list(filter='display_name=\"beans-model-pipeline\"')\n",
+        "for model_id in model_ids:\n",
+        "    model = aiplatform.Model(model_name=model_id.resource_name)\n",
+        "    model.delete()\n",
+        "\n",
+        "# delete locally generated files\n",
+        "! rm -rf beans_deploy_component.yaml beans_model_component.yaml create_dataset.yaml mlmd_pipeline.json\n",
+        "\n",
+        "# delete cloud storage bucket\n",
+        "delete_bucket = False  # set True for deletion\n",
+        "if delete_bucket:\n",
        "    ! gsutil rm -rf {BUCKET_URI}"
      ]
    }