Description

Introduction

Vertex AI Workbench is a managed, Jupyter-based environment in Google Cloud that enables developers and data scientists to build, train, and deploy machine learning models with full integration into the Vertex AI ecosystem. It streamlines development by providing scalable compute, pre-configured ML libraries, and seamless access to data, AutoML, and custom training pipelines. This module introduces users to the full ML workflow using Vertex AI Workbench.

Prerequisites

• Google Cloud Platform (GCP) account with Vertex AI API enabled

• Basic Python and Jupyter notebook experience

• Foundational knowledge of machine learning concepts

• Familiarity with datasets (CSV, structured, or image)

Table of Contents

1. Introduction to Vertex AI Workbench
   1.1 What is Vertex AI Workbench?
   1.2 Key Features and Benefits
   1.3 Types of Notebook Instances (User-Managed vs Managed)
   1.4 Comparison with Other Notebook Solutions

2. Setting Up the Workbench Environment
   2.1 Creating a Notebook Instance
   2.2 Choosing Machine Type and GPU Options
   2.3 Installing and Managing Python Packages
   2.4 Accessing Cloud Storage and BigQuery

3. Data Preparation in Workbench
   3.1 Importing and Exploring Datasets
   3.2 Data Cleaning and Transformation Techniques
   3.3 Using Pandas, NumPy, and TensorFlow Datasets
   3.4 Feature Engineering and Scaling

4. Building ML Models
   4.1 Selecting a Problem Type (Classification, Regression)
   4.2 Building Models using Scikit-learn, TensorFlow, or PyTorch
   4.3 Model Training and Evaluation
   4.4 Logging Metrics and Visualizations

5. Integrating with Vertex AI Services
   5.1 Registering Datasets to Vertex AI
   5.2 Training Jobs using Vertex AI SDK
   5.3 Saving Models to Model Registry
   5.4 Running AutoML Jobs from Workbench

6. Deploying and Testing Models
   6.1 Deploying to Vertex AI Endpoints from Notebooks
   6.2 Running Online and Batch Predictions
   6.3 Calling Deployed Models via REST and Python
   6.4 Versioning and Updating Models

7. Collaboration and Workflow Management
   7.1 Git Integration with Workbench
   7.2 Exporting and Sharing Notebooks
   7.3 Scheduling Notebooks via Cloud Scheduler
   7.4 Using Workbench in Team Environments

8. Monitoring and Maintenance
   8.1 Resource Usage Monitoring
   8.2 Model Monitoring Integration
   8.3 Notebook Lifecycle Management
   8.4 Cleaning Up Resources to Manage Costs

9. Case Studies and Example Projects
   9.1 Customer Churn Prediction Model
   9.2 Image Classification with TensorFlow
   9.3 Sentiment Analysis using Vertex AI Workbench
   9.4 Time Series Forecasting

10. Best Practices and Next Steps
    10.1 Managing Data and Model Lineage
    10.2 Securing Access and IAM Roles
    10.3 Tips for Production Readiness
    10.4 Certifications and Learning Paths

Vertex AI Workbench offers a powerful, cloud-native environment for developing machine learning solutions from start to finish. With seamless integration into Google Cloud’s ecosystem, it allows individuals and teams to build, train, and deploy ML models more efficiently.
Mastering the Workbench equips learners with real-world skills needed to develop robust AI solutions in a collaborative and scalable environment.