Introduction to Google Vertex AI Beginner

Google Vertex AI is Google Cloud's unified machine learning platform that brings together all GCP ML services into a single, cohesive environment. It provides tools for the entire ML workflow — from data preparation and model training to deployment and monitoring.

What is Vertex AI?

Vertex AI is Google Cloud's next-generation ML platform, launched in 2021 as the successor to AI Platform (formerly Cloud ML Engine). It unifies AutoML and custom model training under one roof, providing a streamlined experience for data scientists and ML engineers.

Vertex AI vs AI Platform

If you have used Google Cloud's previous ML offerings, here is how Vertex AI compares:

Feature	AI Platform (Legacy)	Vertex AI
AutoML	Separate products (AutoML Vision, Tables, etc.)	Unified AutoML under one service
Custom Training	AI Platform Training	Integrated custom training with pre-built containers
Prediction	AI Platform Prediction	Unified endpoints with traffic splitting
Pipelines	AI Platform Pipelines (Kubeflow)	Vertex AI Pipelines (managed Kubeflow + TFX)
Feature Store	Not available	Built-in Feature Store
Model Registry	Basic model versioning	Full Model Registry with lineage
Experiments	Limited tracking	Vertex AI Experiments with TensorBoard

Core Components

Datasets

Vertex AI Datasets provide managed storage for your training data. You can import tabular, image, text, and video data, then use it across AutoML and custom training jobs.

Training

Choose between AutoML (automatic model architecture search and hyperparameter tuning) or Custom Training (bring your own code with TensorFlow, PyTorch, scikit-learn, or XGBoost). Custom training supports GPUs, TPUs, and distributed training.

Model Registry

The Model Registry stores trained models with version tracking, metadata, and lineage information. It integrates with model evaluation and deployment workflows.

Endpoints

Deploy models to managed endpoints for real-time predictions. Endpoints support auto-scaling, traffic splitting between model versions, and A/B testing.

Pipelines

Orchestrate ML workflows using Vertex AI Pipelines, which supports both Kubeflow Pipelines and TFX. Automate data processing, training, evaluation, and deployment.

Feature Store

A centralized repository for organizing, storing, and serving ML features. Feature Store ensures consistency between training and serving, and supports both batch and online serving.

Supported ML Frameworks

Framework	Pre-built Container	Custom Container
TensorFlow	Yes (1.x and 2.x)	Yes
PyTorch	Yes	Yes
scikit-learn	Yes	Yes
XGBoost	Yes	Yes
JAX	Yes	Yes
Custom	N/A	Yes (any framework via Docker)

Use Cases

• Computer Vision: Image classification, object detection, and video analysis using AutoML Vision or custom models
• Natural Language Processing: Text classification, entity extraction, and sentiment analysis
• Tabular Data: Classification, regression, and forecasting with AutoML Tables
• Generative AI: Access foundation models through Model Garden, including PaLM 2 and Gemini
• Recommendation Systems: Build and deploy personalized recommendation engines