Getting Started with Vertex AI

Introduction

Vertex AI is Google Cloud’s unified platform for building, training, and deploying machine learning models. It brings data preparation, training, tuning, model registry, and serving into one environment, supporting both AutoML and custom code. Vertex AI is the centerpiece of custom ML on Google Cloud.

Definition

Vertex AI provides a comprehensive set of tools and services for the complete ML lifecycle, from data preparation to model deployment.

Types

Vertex AI Workbench

Managed Jupyter notebooks for ML development

Vertex AI Training

Managed training infrastructure for ML models

Vertex AI Prediction

Managed prediction service for model deployment

Vertex AI Pipelines

ML pipeline orchestration and automation

Use Cases

• End-to-end ML development
• Collaborative ML projects
• Automated ML pipelines
• Model versioning and management
• Production ML deployment

Implementation

Vertex AI supports various ML frameworks including TensorFlow, PyTorch, scikit-learn, and XGBoost.

In Practice

A typical Vertex AI workflow manages datasets, trains models with AutoML or custom containers, tracks experiments, registers models, and deploys them to online or batch endpoints. It also offers feature store, pipelines, and access to foundation models through its model garden.

Key Points

• Unified platform for ML lifecycle
• Managed infrastructure and services
• Integration with Google’s ML research
• Enterprise-grade security and compliance

References

• Vertex AI Documentation — Complete guide to Vertex AI platform