Description

Introduction

Vertex AI’s Model Registry and Experiment Tracking features provide a centralized platform to organize, version, evaluate, and manage ML models and experiments throughout their lifecycle. These tools empower teams to collaborate efficiently, ensure reproducibility, and streamline production workflows.

Prerequisites

• Google Cloud project with Vertex AI enabled

• Familiarity with machine learning workflows

• Experience with model training in Python (e.g., TensorFlow, PyTorch, scikit-learn)

• IAM roles: Vertex AI Admin, Vertex AI Viewer, Storage Admin (optional)

Table of Contents

1. Introduction to Model Management
   1.1 What is a Model Registry?
   1.2 Benefits of Using Vertex AI Model Registry
   1.3 Key Concepts: Versions, Metadata, and Artifacts
   1.4 Role in MLOps Lifecycle

2. Overview of Vertex AI Model Registry
   2.1 Registering Models from Training Jobs
   2.2 Organizing and Browsing Models
   2.3 Adding Metadata and Descriptions
   2.4 Version Control for ML Models

3. Experiment Tracking Basics
   3.1 Why Experiment Tracking Matters
   3.2 Supported Libraries: Keras, scikit-learn, XGBoost, PyTorch
   3.3 Integrating Vertex AI SDK with Training Code
   3.4 Logging Metrics, Parameters, and Artifacts

4. Using Vertex AI Experiments
   4.1 Creating Experiments and Runs
   4.2 Comparing Run Metrics and Performance
   4.3 Visualizing Training Curves and Loss
   4.4 Exporting Reports and Results

5. Automating Model Registration
   5.1 Auto-registering Best Models from Pipelines
   5.2 Attaching Evaluation Metrics and Lineage
   5.3 Creating Custom Evaluation Scripts
   5.4 Using Cloud Functions to Trigger Registrations

6. Deployment from Model Registry
   6.1 Promoting Models to Staging and Production
   6.2 Linking Registered Models to Endpoints
   6.3 Managing Rollbacks and Updates
   6.4 Audit Trails for Governance

7. Collaboration and Access Control
   7.1 Role-Based Access for Teams
   7.2 Tagging and Labeling Models
   7.3 Managing Approval Workflows
   7.4 Cross-Project Sharing with Vertex AI

8. Best Practices
   8.1 Version Everything: Code, Data, Models
   8.2 Naming Conventions and Descriptions
   8.3 Use Cases for Model Lineage
   8.4 Auditability and Reproducibility

9. Integrating with CI/CD and Pipelines
   9.1 CI/CD Triggers for Model Promotion
   9.2 Tracking Experiments in Vertex AI Pipelines
   9.3 Model Registry Integration with Cloud Build
   9.4 Monitoring Promotion Criteria via Metrics

10. Advanced Topics
    10.1 Custom Model Containers and Registry
    10.2 Using Model Registry with BigQuery ML
    10.3 ML Metadata APIs and Lineage Graphs
    10.4 Querying Model Registry with Vertex AI SDK

The Vertex AI Model Registry and Experiment Tracking system ensures traceability, accountability, and performance tracking across the entire ML lifecycle.
It enables collaborative, production-grade machine learning by aligning experimentation with deployment and governance at scale.