Vertex AI Model Builder SDK: Training and Making Predictions on an AutoML Model

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Vertex AI Model Builder SDK: Training and Making Predictions on an AutoML Model

3 hours 5 Credits


In this lab, you learn to use Vertex AI 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 a Vertex AI 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.


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 Qwiklabs using an incognito window.

  2. Note the lab's access time (for example, 02:00:00), 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. Set up your environment

Enable the Vertex AI API

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

Task 2. 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 New Notebook > TensorFlow Enterprise > TensorFlow Enterprise 2.6 (with LTS) > Without GPUs.

  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.

  5. You will see “Build recommended” pop up, click Build. If you see the build failed, ignore it.

Task 3. Clone a course repo within your Vertex AI Notebooks instance

To clone the training-data-analyst notebook in your JupyterLab instance:

  1. In JupyterLab, to open a new terminal, click the Terminal icon.

  2. At the command-line prompt, run the following command:

    git clone
  3. To confirm that you have cloned the repository, double-click on the training-data-analyst directory and ensure that you can see its contents. The files for all the Jupyter notebook-based labs throughout this course are available in this directory.

Task 4. 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. 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. 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.

End your lab

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

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