GSP944

Overview

In this lab, you will train, tune, evaluate, explain, and generate batch and online predictions with a BigQuery ML XGBoost model. You will use a Google Analytics 4 dataset from a real mobile application, Flood it! (Android app, iOS app), to determine the likelihood of users returning to the application. You will generate batch predictions with your BigQuery ML model as well as export and deploy it to Vertex AI for online predictions using the Vertex Python SDK.

BigQuery ML lets you train and do batch inference with machine learning models in BigQuery using standard SQL queries faster by eliminating the need to move data with fewer lines of code.

Vertex AI is Google Cloud's complimentary next generation, unified platform for machine learning development. By developing and deploying BigQuery ML machine learning solutions on Vertex AI, you can leverage a scalable online prediction service and MLOps tools for model retraining and monitoring to significantly enhance your development productivity, the ability to scale your workflow and decision making with your data, and accelerate time to value.

Note: BQML is now BigQuery ML.

This lab is inspired by and extends Churn prediction for game developers using Google Analytics 4 (GA4) and BigQuery ML. Read the blog post and accompanying tutorial for additional depth on this use case and BigQuery ML.

In this lab, you will go one step further and focus on how Vertex AI extends BigQuery ML's capabilities through online prediction so you can incorporate both customer churn predictions into decision making UIs such as Looker dashboards but also online predictions directly into customer applications to power targeted interventions such as targeted incentives.

Objectives

In this lab, you learn how to:

• Explore and preprocess a Google Analytics 4 data sample in BigQuery for machine learning.
• Train a BigQuery ML XGBoost classifier to predict user churn on a mobile gaming application.
• Tune a BigQuery ML XGBoost classifier using BigQuery ML hyperparameter tuning features.
• Evaluate the performance of a BigQuery ML XGBoost classifier.
• Explain your XGBoost model with BigQuery ML Explainable AI global feature attributions.
• Generate batch predictions with your BigQuery ML XGBoost model.
• Export a BigQuery ML XGBoost model to a Google Cloud Storage bucket.
• Upload and deploy a BigQuery ML XGBoost model to a Vertex AI Prediction Endpoint for online predictions.

Setup and requirements

Before you click the Start Lab button

Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click Start Lab, shows how long Google Cloud resources will be made available to you.

This hands-on lab lets you do the lab activities yourself in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials that you use to sign in and access Google Cloud for the duration of the lab.

To complete this lab, you need:

• Access to a standard internet browser (Chrome browser recommended).

Note: Use an Incognito or private browser window to run this lab. This prevents any conflicts between your personal account and the Student account, which may cause extra charges incurred to your personal account.

• Time to complete the lab---remember, once you start, you cannot pause a lab.

Note: If you already have your own personal Google Cloud account or project, do not use it for this lab to avoid extra charges to your account.

Activate Cloud Shell

Cloud Shell is a virtual machine that is loaded with development tools. It offers a persistent 5GB home directory and runs on the Google Cloud. Cloud Shell provides command-line access to your Google Cloud resources.

1. Click Activate Cloud Shell at the top of the Google Cloud console.

When you are connected, you are already authenticated, and the project is set to your Project_ID, . The output contains a line that declares the Project_ID for this session:

Your Cloud Platform project in this session is set to {{{project_0.project_id | "PROJECT_ID"}}}

gcloud is the command-line tool for Google Cloud. It comes pre-installed on Cloud Shell and supports tab-completion.

2. (Optional) You can list the active account name with this command:

gcloud auth list

3. Click Authorize.

Output:

ACTIVE: * ACCOUNT: {{{user_0.username | "ACCOUNT"}}} To set the active account, run: $ gcloud config set account `ACCOUNT`

4. (Optional) You can list the project ID with this command:

gcloud config list project

Output:

[core] project = {{{project_0.project_id | "PROJECT_ID"}}} Note: For full documentation of gcloud, in Google Cloud, refer to the gcloud CLI overview guide.

Task 1. Enable Google Cloud services

• In Cloud Shell, use gcloud commands to enable the services used in the lab:

gcloud services enable \ compute.googleapis.com \ iam.googleapis.com \ iamcredentials.googleapis.com \ monitoring.googleapis.com \ logging.googleapis.com \ notebooks.googleapis.com \ aiplatform.googleapis.com \ bigquery.googleapis.com

Task 2. Deploy Vertex Notebook instance

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

2. Click Enable All Recommended APIs.

3. On the left-hand side, click Workbench.

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

5. Click Create New.

6. 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)

7. Click Create.

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

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

9. Click the Terminal icon to open a terminal window.

Your terminal window will open in a new tab. You can now run commands in the terminal to interact with your Workbench instance.

Your notebook is now set up.

Click Check my progress to verify the objective. Create a Vertex AI Notebook

Task 3. Clone the lab repository

Next you'll clone the training-data-analyst notebook in your JupyterLab instance.

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

Note: If prompted, click Cancel for Build Recommended.

2. To clone the training-data-analyst Github repository, type in the following command, and press Enter:

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

3. To confirm that you have cloned the repository, double-click the training-data-analyst directory and confirm that you can see its contents.

Click Check my progress to verify the objective. Clone the lab repository

Navigate to lab notebook

1. In your notebook, navigate to training-data-analyst > quests > vertex-ai > vertex-bqml, and open lab_exercise.ipynb.

2. Continue the lab in the notebook, and run each cell by clicking the Run () icon at the top of the screen. Alternatively, you can execute the code in a cell with SHIFT + ENTER.

Read the narrative and make sure you understand what's happening in each cell. As you progress through the lab notebook, return back to these instructions to complete the graded exercises.

Task 4. Create a BigQuery dataset

Click Check my progress to verify the objective. Create a BigQuery dataset

Task 5. Create a BigQuery ML XGBoost churn propensity model

Click Check my progress to verify the objective. Create a BigQuery ML XGBoost churn propensity model

Task 6. Evaluate your BigQuery ML model

Click Check my progress to verify the objective. Evaluate your BigQuery ML model

Task 7. Batch predict user churn with your BigQuery ML model

Click Check my progress to verify the objective. Batch predict user churn with your BigQuery ML model

Congratulations!

In this lab you trained, tuned, explained, and deployed a BigQuery ML user churn model to Vertex AI to generate high business impact batch and online churn predictions to target customers likely to churn with interventions such as in-game rewards and reminder notifications.

Next steps / Learn more

Read more about Vertex AI in the Vertex AI Documentation.

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Manual Last Updated December 10, 2024

Lab Last Tested December 10, 2024

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