📚Complete Guide

Google Vertex AI Tutorial: Get Started in 5 Minutes [2026]

Name: Google Vertex AI
Brand: Google Vertex AI
Availability: InStock

Master Google Vertex AI with our step-by-step tutorial, detailed feature walkthrough, and expert tips.

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🔍 Google Vertex AI Features Deep Dive

Explore the key features that make Google Vertex AI powerful for data & analytics workflows.

Model Garden

What it does:

A unified catalog of 180+ foundation and task-specific models, including Gemini, Imagen, Veo, Chirp, Codey, Anthropic's Claude, Meta's Llama, Mistral, and curated open-source models. Each model exposes a consistent API for prediction, tuning, and deployment with shared billing and governance.

Use case:

Gemini API and Long-Context Models

What it does:

Native access to Google's Gemini family, including variants with 1M+ token context windows for processing entire codebases, video, and long documents in a single call. Supports multimodal input across text, images, audio, and video.

Use case:

Vertex AI Agent Builder

What it does:

A higher-level toolkit for building grounded conversational agents, search experiences, and multi-agent workflows. Includes connectors to enterprise data, retrieval-augmented generation, citation, and evaluation tooling.

Use case:

Custom Training on GPU and TPU

What it does:

Managed training jobs on a wide range of accelerators — NVIDIA H100/A100/L4 GPUs and Google TPU v5e/v5p — with distributed training support, hyperparameter tuning (Vizier), and automatic checkpointing.

Use case:

AutoML

What it does:

No-code training for tabular, vision, text, and forecasting tasks. Automates feature engineering, architecture search, and evaluation, producing deployable models without writing training code.

Use case:

Vertex AI Pipelines

What it does:

Managed Kubeflow Pipelines and TFX-based orchestration for reproducible, parameterized ML workflows with lineage tracking and integration with Cloud Build for CI/CD on models.

Use case:

Feature Store

What it does:

Centralized storage and serving of curated features for both training and online prediction, with point-in-time correctness and BigQuery-native ingestion.

Use case:

Model Monitoring and Explainability

What it does:

Production monitoring for data drift, prediction drift, and feature skew, plus Vertex Explainable AI for feature attribution using sampled Shapley, integrated gradients, and XRAI.

Use case:

Enterprise Security and Governance

What it does:

VPC Service Controls, customer-managed encryption keys (CMEK), IAM-based access, audit logging, data residency configuration, and compliance with HIPAA, SOC 2, ISO 27001, and FedRAMP.

Use case:

❓ Frequently Asked Questions

What is the difference between Vertex AI and Google AI Studio?

Google AI Studio is a free, browser-based prototyping tool aimed at individual developers experimenting with Gemini through a simple API key. Vertex AI is the enterprise platform: it runs inside Google Cloud projects with IAM, VPC controls, audit logging, regional data residency, SLAs, and the full MLOps stack. Most production workloads belong on Vertex AI; AI Studio is for prototyping.

Which foundation models are available in Vertex AI Model Garden?

Model Garden includes Google's own Gemini family (Pro, Flash, and long-context variants), Imagen for image generation, Veo for video, Chirp for speech, and Codey for code. Third-party models include Anthropic's Claude, Meta's Llama, Mistral, AI21, and a growing list of open-source and partner models. Availability of specific models can vary by region.

How does Vertex AI pricing work?

Pricing is consumption-based and varies by component. Foundation models are billed per 1K input/output tokens (or per image/second of video). Custom training is billed per machine-hour on the chosen CPU/GPU/TPU configuration. Online prediction endpoints are billed per node-hour while running, batch prediction per job. Storage, Pipelines, Feature Store, and Model Monitoring have their own line items. New customers get GCP free credits, and there is a small always-free tier for experimentation.

Can I fine-tune foundation models on my own data?

Yes. Vertex AI supports supervised fine-tuning on Gemini and several open models, distillation for smaller student models, and RLHF for alignment. Tuned model weights stay within your Google Cloud project, are not used to train Google's base models, and can be deployed to private endpoints with the same governance controls as base models.

Is my data used to train Google's models?

No. Per Google Cloud's customer data terms, prompts, responses, and tuning data submitted to Vertex AI are not used to train or improve Google's foundation models, and customer data is logically isolated within the customer's project. Enterprise controls including CMEK, VPC Service Controls, and data residency settings further restrict where data is processed and stored.

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Tutorial updated March 2026

🔍 Google Vertex AI Features Deep Dive

Explore the key features that make Google Vertex AI powerful for data & analytics workflows.

Model Garden

What it does:

Use case:

Gemini API and Long-Context Models

What it does:

Use case:

Vertex AI Agent Builder

What it does:

Use case:

Custom Training on GPU and TPU

What it does:

Use case:

AutoML

What it does:

No-code training for tabular, vision, text, and forecasting tasks. Automates feature engineering, architecture search, and evaluation, producing deployable models without writing training code.

Use case:

Vertex AI Pipelines

What it does:

Managed Kubeflow Pipelines and TFX-based orchestration for reproducible, parameterized ML workflows with lineage tracking and integration with Cloud Build for CI/CD on models.

Use case:

Feature Store

What it does:

Centralized storage and serving of curated features for both training and online prediction, with point-in-time correctness and BigQuery-native ingestion.

Use case:

Model Monitoring and Explainability

What it does:

Production monitoring for data drift, prediction drift, and feature skew, plus Vertex Explainable AI for feature attribution using sampled Shapley, integrated gradients, and XRAI.

Use case:

Enterprise Security and Governance

What it does:

VPC Service Controls, customer-managed encryption keys (CMEK), IAM-based access, audit logging, data residency configuration, and compliance with HIPAA, SOC 2, ISO 27001, and FedRAMP.