GCP Vertex AI

What is GCP Vertex AI?

Vertex AI is Google Cloud's comprehensive ML platform that unifies data prep, model training, and deployment. It supports both AutoML for quick models and custom training for complex requirements.

Core Components

• AutoML: Automated model training for vision, NLP, and tabular data
• Custom Training: Train models using your own code with Vertex Training
• Model Registry: Manage and version all your models
• Endpoints: Deploy models for real-time predictions
• Batch Predictions: Large-scale batch processing
• Pipelines: Orchestrate ML workflows with Vertex Pipelines
• Feature Store: Centralized feature management

Typical Use Cases

• Computer Vision

  Image classification, object detection, and custom vision models at scale.

• Predictive Analytics

  Time-series forecasting and tabular data prediction for business metrics.

• NLP & Text

  Sentiment analysis, entity extraction, and custom text models.

• ML Ops & Scaling

  Production ML pipelines, monitoring, and continuous model improvement.

Mental Model

Think of Vertex AI as a complete ML factory. You bring data and code, and it automates the hard parts—training on optimized hardware, tuning hyperparameters, managing model versions, and deploying at scale. It handles the plumbing so you focus on your model's logic.

Architecture Overview

[Data Source]
    ↓
[Data Processing]
    ↓
[Training Jobs] (AutoML or Custom)
    ↓
[Model Registry]
    ↓
[Deployment]
  ├─ Online Endpoints (Real-time)
  ├─ Batch Predictions (Offline)
  └─ Pipelines (Orchestration)
    ↓
[Monitoring & Evaluation]
      

Vertex AI orchestrates the complete ML lifecycle—from data preparation through training, deployment, and monitoring. Multi-cloud infrastructure enables model training and serving at enterprise scale.

Key Concepts Glossary

• AutoML: Automated training where Vertex optimizes model architecture and hyperparameters
• Custom Training: Train models using your own code with managed infrastructure
• Endpoint: Deployed model serving real-time or batch predictions
• Pipeline: Workflow orchestrating data processing, training, and deployment steps
• Feature Store: Centralized management of features for training and serving

When to Use Vertex AI

Choose Vertex AI if you need:

• Managed ML infrastructure with minimal DevOps overhead
• AutoML for rapid model development without deep expertise
• Enterprise-grade monitoring, versioning, and governance

Consider alternatives if:

• You need multi-cloud flexibility beyond Google Cloud
• You prefer open-source tools for complete control

Getting Started

Install Google Cloud SDK and authenticate:

pip install google-cloud-aiplatform
gcloud auth login

→ Vertex AI Documentation

Resources for Further Learning

• Official Documentation - Complete reference and guides
• GitHub Repository - Sample code and examples
• Tutorials - Step-by-step guides for different use cases
• Google Colab - Free notebook environment for experimenting