Overview

Duration is 1 min

In this lab, you will perform advanced feature engineering by executing feature cross using BigQuery's ML.FEATURE_CROSS, deriving coordinate features and feature crossing coordinate features.

Additionally, you will apply the BUCKETIZE function, apply the TRANSFORM clause, apply L2 Regularization and evaluate model performance throughout the process.

What you learn

In this lab, you will:

• Create a Workbench Instance Notebook.
• Create SQL statements to evaluate the model
• Extract temporal features
• Perform a feature cross on temporal features
• Apply ML.FEATURE_CROSS to categorical features
• Create a Euclidan feature column
• Feature cross coordinate features
• Apply the BUCKETIZE function
• Apply the TRANSFORM clause and L2 Regularization
• Evaluate the model using ML.PREDICT

Vertex AI offers two Notebook Solutions, Workbench and Colab Enterprise.

Workbench

Vertex AI Workbench is a good option for projects that prioritize control and customizability. It’s great for complex projects spanning multiple files, with complex dependencies. It’s also a good choice for a data scientist who is transitioning to the cloud from a workstation or laptop.

Vertex AI Workbench Instances comes with a preinstalled suite of deep learning packages, including support for the TensorFlow and PyTorch frameworks.

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. Enable APIs

1. On the Navigation menu (), click APIs & services.

2. Scroll down and confirm that your APIs are enabled.

3. If an API is missing, click ENABLE APIS AND SERVICES at the top, search for the API by name, and enable it for your project.

• BigQuery

Task 2. Launch Vertex AI Workbench instance

1. In the Google Cloud console, from the Navigation menu (), select Vertex AI > Dashboard.

2. Click Enable All Recommended APIs.

3. In the Navigation menu, click 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 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 Vertex AI Workbench notebook.

Click Check my progress to verify the objective. Launch Vertex AI Workbench instance

Task 3. Clone a course repo within your JupyterLab interface

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 JupyterLab interface

Task 4. Perform advanced Feature Engineering in BigQuery ML

Duration is 60 min

1. In the notebook interface, navigate to training-data-analyst > courses > machine_learning > deepdive2 > feature_engineering > labs and opening 2_bqml_adv_feat_eng-lab.ipynb.

2. A pop-up will appear for you to select a kernel. Choose the TensorFlow 2.11 (Local) kernel from the options.

3. In the notebook interface, click on Edit > Clear All Outputs (click on Edit, then in the drop-down menu, select Clear All Outputs).

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

Click Check my progress to verify the objective. Perform advanced Feature Engineering in BigQuery ML

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.

You will be given an opportunity to rate the lab experience. Select the applicable number of stars, type a comment, and then click Submit.

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• 3 stars = Neutral
• 4 stars = Satisfied
• 5 stars = Very satisfied

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