Vertex AI is Google Cloud’s unified AI/ML platform — a single place where you can build, deploy, train, and manage machine learning models and AI applications at enterprise scale.

Think of it as Google’s answer to Azure AI + AWS SageMaker — it brings together everything an AI team needs under one roof.

The Core Idea

Before Vertex AI, Google had many scattered AI tools:

AI Platform (training)
AutoML (no-code ML)
AI Hub (model sharing)
Notebooks (experimentation)
Predictions (serving)

Vertex AI unified all of them into one platform in 2021.

Vertex AI — Main Components## What is Vertex AI?

Vertex AI is Google Cloud’s fully managed, unified AI/ML platform — a single place to build, train, deploy, and manage machine learning models and generative AI applications at enterprise scale.

The 4 Main Pillars

1. Data

Everything starts with data. Vertex AI provides tools to manage, label, and store training data in a structured way.

• Datasets — upload and manage structured, image, video, text, or tabular data
• Feature Store — a centralized repository to store and share ML features across teams, avoiding redundant computation
• Data Labeling — human-in-the-loop tool to annotate training data (images, text, video)
• BigQuery ML — run ML models directly inside BigQuery using SQL, no data movement needed

2. Build

Where models are actually created — either automatically or with full custom code.

• AutoML — no-code model training; you bring data, Google finds the best model architecture automatically
• Custom training — full control; use TensorFlow, PyTorch, scikit-learn, or any framework on managed compute
• Workbench — managed JupyterLab notebooks with GCP integrations pre-wired
• Colab Enterprise — Google Colab but enterprise-grade, with IAM, VPC, and persistent storage

3. Deploy

Serving models to production reliably and at scale.

• Endpoints — deploy models as REST APIs with autoscaling, A/B testing, and traffic splitting
• Batch prediction — run predictions on large datasets offline without a live endpoint
• Model registry — versioned catalog of all your trained models with lineage tracking
• Explainability — understand why a model made a prediction (feature attribution)

4. MLOps

The operational layer that makes ML repeatable and production-grade.

• Pipelines — orchestrate end-to-end ML workflows (data → train → evaluate → deploy) as DAGs
• Experiments — track hyperparameters, metrics, and artifacts across training runs
• Model monitoring — detect data drift and prediction drift in production automatically
• Metadata — full lineage tracking of every artifact, dataset, and model version

Generative AI Layer

On top of classical ML, Vertex AI has a dedicated generative AI tier:

• Model Garden — a catalog of 130+ foundation models (Gemini, Llama, Claude, Mistral, etc.) ready to use or fine-tune
• Gemini API — access Google’s most capable multimodal model (text, images, video, code, audio)
• Vertex AI Studio — a UI playground to prompt, test, and compare models without writing code
• Embeddings API — convert text into vectors for semantic search and RAG (text-embedding-004)

Vertex AI Search + Vector Search

A specialized layer for RAG and semantic search:

• Vertex AI Search — fully managed search engine over your documents, grounded in your data
• Vector Search — high-scale approximate nearest neighbor (ANN) search, stores and queries billions of vectors using Google’s ScaNN algorithm

This is what powers the GCP RAG pipeline from the previous article.

Vertex AI vs Competitors

Key Takeaway

Vertex AI is to machine learning what Google Cloud is to infrastructure — fully managed, deeply integrated, and designed to scale from prototype to production without switching tools. Whether you’re training a custom model, deploying Gemini, or building a RAG pipeline with vector search, it all lives under one unified platform with shared IAM, billing, and networking.