Overview

In this lab, you learn to use Agent Platform Python client library to train and make predictions on an AutoML model based on a tabular dataset. Alternatively, you can train and make predictions on models by using the gcloud command-line tool or by using the online Cloud Console.

Learning objectives

• Create an Agent Platform model training job.
• Train an AutoML tabular model.
• Deploy the model resource to a serving endpoint resource.
• Make a prediction by sending data.
• Undeploy the model resource.

Setup

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.

Task 1. Launch an Agent Platform Workbench instance

1. In the Google Cloud console, from the Navigation menu (), select Agent Platform > Overview.

2. Click Enable APIs > Enable.

3. From the left navigation pane, click Notebooks > Workbench.

   At the top of the Workbench page, ensure you are in the Instances view.

4. Click Create New.

5. Configure the Instance:

   • Name: lab-workbench
   • Region: Set the region to
   • Zone: Set the zone to
   • Advanced Options (Optional): If needed, click "Advanced Options" for further customization (e.g., machine type, disk size).

6. Click Create.

This will take a few minutes to create the instance. A green checkmark will appear next to its name when it's ready.

7. Click Open Jupyterlab next to the instance name to launch the JupyterLab interface. This will open a new tab in your browser.

8. Click the Python 3 (Local) icon to launch a new Python notebook.

9. Right-click on the Untitled.ipynb file in the menu bar and select Rename Notebook to give it a meaningful name.

Your environment is set up. You are now ready to start working with your Agent Platform Workbench notebook.

Click Check my progress to verify the objective. Launch an Agent Platform Workbench instance.

Task 2. Clone a course repo within your Agent Platform Workbench 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.

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

Click Check my progress to verify the objective. Clone a course repo within your Agent Platform Workbench instance.

Task 3. Train and make predictions on an AutoML model

1. In the notebook interface, navigate to training-data-analyst > courses > machine_learning > deepdive2 > how_google_does_ml > labs, and open automl-tabular-classification.ipynb.

2. A pop-up will appear for you to select a kernel. Choose the Python 3 (Local) kernel from the options.

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

4. 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. Other cell commands are listed in the notebook UI under Run.

• Hints may also be provided for the tasks to guide you along. Highlight the text to read the hints (they are in white text).
• If you need more help, look at the complete solution at training-data-analyst > courses > machine_learning > deepdive2 > how_google_does_ml > solutions, and open automl-tabular-classification.ipynb.

Click Check my progress to verify the objective. Train and make predictions on an AutoML model.

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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• 5 stars = Very satisfied

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