Quick Setup to Explore Google’s Vertex AI Workbench

Google’s Vertex AI is rapidly evolving, offering a comprehensive platform for building, deploying, and scaling machine learning models. With Vertex AI, you can do a lot—like training TensorFlow models, performing hyperparameter tuning, deploying models for online predictions, and more. However, setting up the necessary APIs for a TensorFlow Jupyter Notebook can be tricky. This quick tutorial will guide you through enabling the right APIs, creating a service account, and granting the necessary permissions to ensure a smooth exploration experience.

Step 1: Enable the Required APIs

When you launch Google Cloud, click the terminal or console icon in the top right corner. Once in the console, run the following command to enable the essential APIs:

?? Enable APIs Command

Here’s a brief overview of the APIs we’re enabling:

• Compute Engine API (compute.googleapis.com): Manages virtual machines, disks, and networks.
• Identity and Access Management API (iam.googleapis.com): Handles access control policies for Google Cloud resources.
• IAM Service Account Credentials API (iamcredentials.googleapis.com): Manages service account credentials, including access tokens.
• Cloud Monitoring API (monitoring.googleapis.com): Monitors applications and services, setting up alerts.
• Cloud Logging API (logging.googleapis.com): Manages and analyzes log data from your Google Cloud resources.
• AI Platform Notebooks API (notebooks.googleapis.com): Manages Jupyter Notebooks for running ML models on Google Cloud.
• AI Platform API (aiplatform.googleapis.com): Provides tools for building, deploying, and managing ML models at scale.
• BigQuery API (bigquery.googleapis.com): Accesses Google’s managed data warehouse for running SQL queries on large datasets.
• Artifact Registry API (artifactregistry.googleapis.com): Stores and manages build artifacts like Docker images and packages.
• Cloud Build API (cloudbuild.googleapis.com): Automates building, testing, and deploying applications.
• Kubernetes Engine API (container.googleapis.com): Manages GKE clusters for deploying and scaling containerized applications.

Step 2: Set Up a Service Account

Next, you’ll need to securely grant access to these APIs with a service account. TensorFlow and TensorBoard are common tools in many AI workflows, so we’ll enable service account access for these tools.

?? Enable Service Account Command

Step 3: Grant Cloud Storage Access

To read and write TensorBoard logs, you’ll need to grant Cloud Storage access. This is done by binding the Cloud Storage Admin role to your project.

?? Grant Cloud Storage Access Command

Step 4: Grant BigQuery Access

If you plan to use BigQuery to read from your TensorFlow models, you’ll need to grant access to BigQuery as well. It’s a similar process, but you’ll notice we set PROJECT_ID as an environment variable to avoid hardcoding.

?? Grant BigQuery Access Command

Step 5: Grant Vertex AI Access

Finally, grant access to Vertex AI so you can fully leverage its capabilities.

?? Grant Vertex AI Access Command

Conclusion: Explore Vertex AI Tutorials

With these steps completed, you’re ready to explore Vertex AI. Whether you’re training TensorFlow models or deploying them for predictions, you now have everything set up for a smooth experience.

For more hands-on learning, check out Google’s official tutorials for Vertex AI with TensorFlow and Jupyter Notebooks:

?? Vertex AI Tutorials

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