Overview

In this lab, you will 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 will 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.

Vertex AI is Google Cloud's next generation, unified platform for machine learning development and the successor to AI Platform announced at Google I/O in May 2021. By developing machine learning solutions on Vertex AI, you can leverage the latest ML pre-built components and AutoML to significantly enhance development productivity, the ability to scale your workflow and decision making with your data, and accelerate time to value.

Learning objectives

• Train a TensorFlow model locally in a hosted Vertex AI Workbench.
• Create a managed Tabular dataset artifact for experiment tracking.
• Containerize your training code with Cloud Build and push it to Google Cloud Artifact Registry.
• Run a Vertex AI custom training job with your custom model container.
• Use Vertex TensorBoard to visualize model performance.
• Deploy your trained model to a Vertex Online Prediction Endpoint for serving predictions.
• Request an online prediction and explanation and see the response.

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 Qwiklabs 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.

Activate Cloud Shell

Cloud Shell is a virtual machine that contains development tools. It offers a persistent 5-GB home directory and runs on Google Cloud. Cloud Shell provides command-line access to your Google Cloud resources. gcloud is the command-line tool for Google Cloud. It comes pre-installed on Cloud Shell and supports tab completion.

1. Click the Activate Cloud Shell button () at the top right of the console.

2. Click Continue.
   It takes a few moments to provision and connect to the environment. When you are connected, you are also authenticated, and the project is set to your PROJECT_ID.

Sample commands

• List the active account name:

gcloud auth list

(Output)

Credentialed accounts: - <myaccount>@<mydomain>.com (active)

(Example output)

Credentialed accounts: - google1623327_student@qwiklabs.net

• List the project ID:

gcloud config list project

(Output)

[core] project = <project_ID>

(Example output)

[core] project = qwiklabs-gcp-44776a13dea667a6 Note: Full documentation of gcloud is available in the gcloud CLI overview guide.

Task 1. Enable Google Cloud services

• In Cloud Shell, 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

Task 2. Create Vertex AI custom service account for Vertex Tensorboard integration

1. Create custom service account:

SERVICE_ACCOUNT_ID=vertex-custom-training-sa gcloud iam service-accounts create $SERVICE_ACCOUNT_ID \ --description="A custom service account for Vertex custom training with Tensorboard" \ --display-name="Vertex AI Custom Training"

2. Grant it access to Cloud Storage for writing and retrieving Tensorboard logs:

PROJECT_ID=$(gcloud config get-value core/project) gcloud projects add-iam-policy-binding $PROJECT_ID \ --member=serviceAccount:$SERVICE_ACCOUNT_ID@$PROJECT_ID.iam.gserviceaccount.com \ --role="roles/storage.admin"

3. Grant it access to your BigQuery data source to read data into your TensorFlow model:

gcloud projects add-iam-policy-binding $PROJECT_ID \ --member=serviceAccount:$SERVICE_ACCOUNT_ID@$PROJECT_ID.iam.gserviceaccount.com \ --role="roles/bigquery.admin"

4. Grant it access to Vertex AI for running model training, deployment, and explanation jobs:

gcloud projects add-iam-policy-binding $PROJECT_ID \ --member=serviceAccount:$SERVICE_ACCOUNT_ID@$PROJECT_ID.iam.gserviceaccount.com \ --role="roles/aiplatform.user"

Task 3. Launch Vertex AI Workbench notebook

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 + Create New.

3. In the Create instance dialog, confirm the name of the deep learning VM.

4. Change the region to and zone to .

5. For Environment, and select TensorFlow Enterprise 2.6 (with LTS and Intel MKL-DNN/MKL) from the dropdown.

6. Click Advance Option, then click Machine type, and select E2 > e2-standard-2.

7. Leave all settings as they are and then click Create. The new VM will take 2-3 minutes to start.

8. Click Open JupyterLab.
   A JupyterLab window will open in a new tab.

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

Task 4. Clone the lab repository

Next 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 the first cell of your notebook 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 5. Install lab dependencies and Run the Notebook

• Run the following to go to the training-data-analyst/self-paced-labs/vertex-ai/vertex-ai-qwikstart folder, then pip3 install requirements.txt to install lab dependencies:

cd training-data-analyst/self-paced-labs/vertex-ai/vertex-ai-qwikstart pip3 install --user -r requirements.txt Note: Ignore the incompatibility warnings and errors.

Navigate to lab notebook

1. In your notebook, navigate to training-data-analyst > self-paced-labs > vertex-ai > vertex-ai-qwikstart, and open lab_exercise_long.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.

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. You progressed from training a TensorFlow model locally to training on the cloud with Vertex AI and leveraged several new unified platform capabilities such as Vertex TensorBoard and prediction feature attributions.

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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