Description

Introduction

Vertex AI Pipelines enable end-to-end automation of machine learning workflows, from data ingestion to model deployment. Built on Kubeflow Pipelines, Vertex AI Pipelines offer reproducibility, scalability, and traceability. This module guides you through designing, deploying, and managing ML pipelines using Vertex AI.

Prerequisites

• Google Cloud project with Vertex AI and Workbench enabled

• Familiarity with Python, Jupyter notebooks, and basic ML concepts

• Basic understanding of CI/CD or workflow automation

• Vertex AI SDK and Kubeflow Pipelines experience is helpful

Table of Contents

1. Overview of Vertex AI Pipelines
   1.1 What Are ML Pipelines?
   1.2 Benefits of Using Vertex AI Pipelines
   1.3 Core Concepts: Pipeline Components, DAGs, Artifacts
   1.4 Comparison with Manual and Scripted Workflows

2. Pipeline Architecture and Building Blocks
   2.1 Pipeline Components and Reusability
   2.2 Pipeline Inputs and Outputs
   2.3 Understanding Pipeline DAGs
   2.4 Orchestration and Metadata Tracking

3. Developing Pipelines with the SDK
   3.1 Setting Up the SDK and Workbench
   3.2 Authoring Pipelines in Python
   3.3 Defining Custom Components
   3.4 Compiling and Validating Pipelines

4. Running and Managing Pipelines
   4.1 Submitting Pipelines to Vertex AI
   4.2 Monitoring Pipeline Executions
   4.3 Visualizing Steps and Lineage
   4.4 Managing Pipeline Versions and Parameters

5. Integrating Prebuilt Components
   5.1 Using Google-Provided Components (e.g., BigQuery, AutoML)
   5.2 Data Preprocessing with Vertex Dataflow
   5.3 Model Training and Evaluation in Pipelines
   5.4 Model Deployment as Final Step

6. Scheduling and Automating Pipelines
   6.1 Using Cloud Scheduler with Pipelines
   6.2 Event-Driven Triggers with Cloud Functions
   6.3 Managing Retries and Failures
   6.4 Notifications and Logging Integration

7. Secure and Scalable Execution
   7.1 IAM Permissions for Pipeline Components
   7.2 Isolating Components with Service Accounts
   7.3 VPC Support for Secure Workflows
   7.4 Scaling and Quotas for Large Pipelines

8. CI/CD Integration
   8.1 Creating Pipelines in DevOps Workflows
   8.2 GitHub Actions and Cloud Build Integration
   8.3 Automated Testing and Promotion
   8.4 Reproducibility and Version Control

9. Real-World Use Cases
   9.1 Data Engineering Pipeline for Retail Forecasting
   9.2 End-to-End NLP Model Automation
   9.3 Computer Vision Pipelines with Image Augmentation
   9.4 Periodic Model Retraining in Production

10. Best Practices and Optimization
    10.1 Modularizing and Reusing Components
    10.2 Efficient Resource Management
    10.3 Tracking Metadata and Metrics
    10.4 Common Pitfalls and Debugging Tips

Vertex AI Pipelines offer a robust and scalable way to automate ML workflows, reduce manual effort, and ensure reproducibility.
By integrating components, metadata tracking, and pipeline orchestration, teams can deliver more reliable and faster ML depl