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Vertex AI: Qwik Start

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Vertex AI: Qwik Start

1 hour 30 minutes 5 Credits

GSP917

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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-overview.png

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.

Objectives

Setup

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 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 Console. 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 from the Lab Details panel and paste it into the Sign in dialog. Click Next.

  4. Copy the Password from the Lab Details panel and paste it into the Welcome dialog. Click Next.

    Important: You must use the credentials from the left panel. Do not use your Google Cloud Skills Boost credentials. Note: Using your own Google Cloud account for this lab may incur extra charges.
  5. 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 Cloud Console opens in this tab.

Note: You can view the menu with a list of Google Cloud Products and Services by clicking the Navigation menu at the top-left. Navigation menu icon

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. In the Cloud Console, in the top right toolbar, click the Activate Cloud Shell button.

Cloud Shell icon

  1. Click Continue.

It takes a few moments to provision and connect to the environment. 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 YOUR_PROJECT_ID

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

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

gcloud auth list

(Output)

ACTIVE: * ACCOUNT: student-01-xxxxxxxxxxxx@qwiklabs.net To set the active account, run: $ gcloud config set account `ACCOUNT`
  1. (Optional) You can list the project ID with this command:

gcloud config list project

(Output)

[core] project = <project_ID>

(Example output)

[core] project = qwiklabs-gcp-44776a13dea667a6 For full documentation of gcloud, in Google Cloud, Cloud SDK documentation, see the gcloud command-line tool overview.

Enable Google Cloud services

  1. 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

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 $SERVICE_ACCOUNT_ID \ --description="A custom service account for Vertex custom training with Tensorboard" \ --display-name="Vertex AI Custom Training"
  1. Grant it access to GCS for writing and retrieving Tensorboard logs
PROJECT_ID=$(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"
  1. 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"
  1. 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"

Deploy Vertex Notebook instance

  1. Click on the Navigation Menu.

  2. Navigate to Vertex AI, then to Workbench

vertex-ai-workbench.png

  1. On the Notebook instances page, navigate to the User-Managed Notebooks tab and click New Notebook.

  2. In the Customize instance menu, select TensorFlow Enterprise and choose the version of TensorFlow Enterprise 2.3 (with LTS) > Without GPUs.

vertex-notebook-create-2.png

  1. In the New notebook dialog, for Region, select us-central1, for Zone, select a zone within the selected region, leave all other fields with their default options, and click Create.

After a few minutes, the Vertex AI console will display your instance name, followed by Open Jupyterlab.

  1. Click Open JupyterLab. Your notebook is now set up.

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

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.
  1. 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
  1. To confirm that you have cloned the repository, double-click the training-data-analyst directory and confirm that you can see its contents.

repo-directory.png

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

Install lab dependencies

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

cd training-data-analyst/self-paced-labs/vertex-ai/vertex-ai-qwikstart pip install -U -r requirements.txt

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.ipynb.

lab-directory.png

  1. Continue the lab in the notebook, and run each cell by clicking the Run (run-button.png) 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!

Congratulations! In this lab, you walked through a machine learning experimentation workflow using Google Cloud's BigQuery for data storage and analysis and Vertex AI's 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.

completion_badge_Baseline_Data_ML_AI-135 completion_badge_ML_Infrastructure-135

Finish Your Quest

This self-paced lab is part of the Qwiklabs Baseline: Data, ML, AI , Advanced ML: ML Infrastructure Quests.

This self-paced lab is also part of the Build and Deploy Machine Learning Solutions on Vertex AI skill badge Quest. Completing this skill badge quest earns you the badge above, to recognize your achievement. Share your badge on your resume and social platforms, and announce your accomplishment using #GoogleCloudBadge.

search the catalog for 20+ other skill badge quests in which you can enroll.

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Manual Last Updated June 10, 2022
Lab Last Tested June 10, 2022

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