Overview

This lab demonstrates how to use the Vertex SDK to train and deploy a custom tabular regression model for online prediction with explanation.

Learning objectives

In this lab, you learn how to create a custom model from a Python script in a Google prebuilt Docker container using the Vertex SDK, and then do a prediction with explanations on the deployed model by sending data. You can alternatively use the Google Cloud CLI or the Google Cloud Console to create custom models using.

You perform the following tasks:

• Create a Vertex custom job for training a model.
• Train a TensorFlow model.
• Retrieve and load the model artifacts.
• View the model evaluation.
• Set explanation parameters.
• Upload the model as a Vertex Model resource.
• Deploy the Model resource to a serving Endpoint resource.
• Make a prediction with explanation.
• Undeploy the Model resource.

Setup and requirements

For each lab, you get a new Google Cloud project and set of resources for a fixed time at no cost.

1. Sign in to Google Skills using an incognito window.

2. Note the lab's access time (for example, 1:15:00), and make sure you can finish within that time.
   There is no pause feature. You can restart if needed, but you have to start at the beginning.

3. When ready, click Start lab.

4. Note your lab credentials (Username and Password). You will use them to sign in to the Google Cloud Console.

5. Click Open Google Console.

6. Click Use another account and copy/paste credentials for this lab into the prompts.
   If you use other credentials, you'll receive errors or incur charges.

7. Accept the terms and skip the recovery resource page.

Enable the Vertex AI API

• In the Google Cloud Console, on the Navigation menu, click Vertex AI > Dashboard, and then click Enable Vertex AI API.

Task 1. Launch a Vertex AI Notebooks instance

1. In the Google Cloud Console, on the Navigation Menu, click Vertex AI > Workbench. Select User-Managed Notebooks.

2. On the Notebook instances page, Click Create New and choose the latest version of TensorFlow Enterprise 2.6 (with LTS) in Environment.

3. In the New notebook instance dialog, confirm the name of the deep learning VM, if you don’t want to change the region and zone, leave all settings as they are and then click Create. The new VM will take 2-3 minutes to start.

4. Click Open JupyterLab.
   A JupyterLab window will open in a new tab.

Task 2. Clone a course repo within your Vertex AI Notebooks instance

The GitHub repo contains both the lab file and solutions files for the course.

1. Copy and run the following code in the first cell of your notebook to clone the training-data-analyst repository.

!git clone https://github.com/GoogleCloudPlatform/training-data-analyst

2. Confirm that you have cloned the repository. Double-click on the training-data-analyst directory and ensure that you can see its contents.

Task 3. Train and deploy a custom tabular regression model for online prediction with explanation

1. In the notebook interface, navigate to training-data-analyst > courses > machine_learning > deepdive2 > machine_learning_in_the_enterprise > labs, and open sdk_custom_tabular_regression_online_explain.ipynb.

2. In the notebook interface, click Edit > Clear All Outputs.

3. Carefully read through the notebook instructions and fill in lines marked with #TODO where you need to complete the code.

Tip: To run the current cell, click the cell and press SHIFT+ENTER.

To view the complete solution, navigate to training-data-analyst > courses > machine_learning > deepdive2 > machine_learning_in_the_enterprise > solutions, and open sdk_custom_tabular_regression_online_explain.ipynb.

End your lab

When you have completed your lab, click End Lab. Google Skills removes the resources you’ve used and cleans the account for you.

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