GSP917

Overview

In this lab, you use BigQuery for data processing and exploratory data analysis and the Vertex AI platform to train and deploy a custom TensorFlow Regressor model to predict customer lifetime value. The goal of the lab is to introduce to Vertex AI through a high value real world use case - predictive CLV. You start with a local BigQuery and TensorFlow workflow that you may already be familiar with and progress toward training and deploying your model in the cloud with Vertex AI.

Objectives

In this lab, you will:

• Train a TensorFlow model locally in a hosted Vertex Notebook.
• Use Vertex TensorBoard to visualize model performance.

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.

How to start your lab and sign in to the Google Cloud console

1. Click the Start Lab button. If you need to pay for the lab, a pop-up opens for you to select your payment method. On the left is the Lab Details panel with the following:

   • The Open Google Cloud console button
   • Time remaining
   • The temporary credentials that you must use for this lab
   • Other information, if needed, to step through this lab

2. Click Open Google Cloud console (or right-click and select Open Link in Incognito Window if you are running the Chrome browser).

   The lab spins up resources, and then opens another tab that shows the Sign in page.

   Tip: Arrange the tabs in separate windows, side-by-side.

   Note: If you see the Choose an account dialog, click Use Another Account.

3. If necessary, copy the Username below and paste it into the Sign in dialog.

   {{{user_0.username | "Username"}}}

   You can also find the Username in the Lab Details panel.

4. Click Next.

5. Copy the Password below and paste it into the Welcome dialog.

   {{{user_0.password | "Password"}}}

   You can also find the Password in the Lab Details panel.

6. Click Next.

   Important: You must use the credentials the lab provides you. Do not use your Google Cloud account credentials. Note: Using your own Google Cloud account for this lab may incur extra charges.

7. Click through the subsequent pages:

   • Accept the terms and conditions.
   • Do not add recovery options or two-factor authentication (because this is a temporary account).
   • Do not sign up for free trials.

After a few moments, the Google Cloud console opens in this tab.

Note: To view a menu with a list of Google Cloud products and services, click the Navigation menu at the top-left.

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

1. Open a new Cloud Shell terminal by clicking the Cloud Shell icon in the top right corner of the Google Cloud Console.

2. In your Cloud Shell terminal, use gcloud 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 \ artifactregistry.googleapis.com \ cloudbuild.googleapis.com \ container.googleapis.com

Click Check my progress to verify the objective. Enable Google Cloud services.

Task 2. Create a Vertex AI Workbench 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: Provide a name for your instance or leave the default value
   • 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.

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

Task 3. Clone the lab repository

In this section, you'll clone the training-data-analyst repo to your JupyterLab instance. The GitHub repo contains both the lab file and solutions files for the course.

1. Copy and run the following code in your terminal 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.

It will take several minutes for the repo to clone.

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

Task 4. Install lab dependencies

1. In the open terminal, run the following command to install the lab dependencies:

cd training-data-analyst/self-paced-labs/vertex-ai/vertex-ai-qwikstart pip3 install --user -r requirements.txt sudo apt -y install python3-pandas sudo apt -y install graphviz pip uninstall openpyxl pip install openpyxl

2. When prompted, type y and press Enter to confirm the installation.

Task 5. Run the lab notebook

1. In the file browser, navigate to training-data-analyst/self-paced-labs/vertex-ai/vertex-ai-qwikstart, and open lab_exercise.ipynb.

2. When prompted, select the Python 3 kernel.

Note: Be sure to open the notebook titled lab_exercise.ipynb to complete this lab.

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.

Click Check my progress to verify the objective. Run the lab notebook.

Congratulations!

In this lab, you ran a machine learning experimentation workflow using Google Cloud BigQuery for data storage and analysis and Vertex AI machine learning services to train and deploy a TensorFlow model to predict customer lifetime value.

Next steps / learn more

• Learn more about Vertex AI.
• Check out the Generative AI on Vertex AI documentation.
• Learn more about Generative AI on the Google Cloud Tech YouTube channel.
• Google Cloud Generative AI official repo
• Example Gemini notebooks

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Manual Last Updated October 7, 2024

Lab Last Tested October 7, 2024

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