Google Vertex AI – ML Platform & Model Garden

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Table of Contents hide
Google Cloud Vertex AI – ML Platform Overview
Vertex AI Key Components
Model Garden
Vertex AI Studio
Model Training
Model Deployment
Vertex AI Pipelines
Vertex AI Feature Store
Vertex AI Vector Search
Vertex AI Agent Builder
Grounding
Model Evaluation
Vertex AI Workbench
MLOps
Pricing Overview
Vertex AI vs AWS SageMaker vs Azure ML
GCP Certification Exam Practice Questions

Google Cloud Vertex AI – ML Platform Overview

  • Vertex AI is Google Cloud’s unified machine learning platform that brings together all the tools and services needed to build, deploy, and scale ML models and AI applications.
  • Vertex AI consolidates previously separate Google Cloud AI services (AI Platform, AutoML, Dialogflow) into a single, cohesive platform.
  • In April 2026, Google rebranded Vertex AI as the Gemini Enterprise Agent Platform at Google Cloud Next ’26, shifting focus toward an agent-first architecture while retaining all existing ML/AI capabilities.
  • Vertex AI supports the entire ML lifecycle: data preparation, model training, evaluation, deployment, monitoring, and management.
  • Integrates natively with BigQuery, Cloud Storage, Dataflow, and other Google Cloud services.
  • Supports both traditional ML (tabular, vision, NLP) and generative AI (foundation models, LLMs, multimodal).
  • Provides both no-code/low-code (AutoML, Studio) and code-first (custom training, SDK, notebooks) approaches.

Vertex AI Key Components

  • Model Garden – Access to 200+ foundation models (first-party, third-party, open)
  • Vertex AI Studio – Interactive prompt design, testing, and tuning interface
  • Model Training – AutoML and custom training with GPUs/TPUs
  • Model Deployment – Online and batch prediction endpoints
  • Vertex AI Pipelines – ML workflow orchestration (Kubeflow/TFX)
  • Feature Store – Centralized feature management integrated with BigQuery
  • Vector Search – High-performance similarity search using ScaNN
  • Agent Builder – Build, deploy, and govern AI agents
  • Grounding – Connect models to real-time data sources
  • Model Evaluation – Comprehensive model assessment tools
  • Workbench – Managed Jupyter notebook environments
  • MLOps – Model Registry, Experiments, Monitoring

Model Garden

  • Model Garden provides access to 200+ curated foundation models, organized into three categories:
    • First-party models – Google’s own models: Gemini (text, code, multimodal), Imagen (image generation), Veo (video generation), Chirp (speech), Gemma (open models), Lyria (music)
    • Third-party models – Partner models: Anthropic’s Claude family, Mistral AI models
    • Open models – Community models: Meta’s Llama 3.2, Gemma 3, and others deployable on Google Cloud infrastructure
  • First-party and select third-party models are available as managed APIs (serverless, no infrastructure management required).
  • Open models can be deployed to dedicated endpoints with custom hardware configurations.
  • Model Garden supports one-click deployment, fine-tuning notebooks, and optimization features for open models.
  • Key Gemini models available:
    • Gemini 2.5 Flash – Fast, cost-efficient model for everyday tasks
    • Gemini 2.5 Pro – Advanced reasoning and complex tasks
    • Gemini 3 Flash / Gemini 3 Pro – Latest generation (2026)
  • Supports multimodal inputs: text, images, video, audio, and code.
  • Enterprise features: data residency controls, VPC Service Controls, customer-managed encryption keys (CMEK), and audit logging.

Vertex AI Studio

  • Vertex AI Studio is a web-based interface for rapidly prototyping and testing generative AI models.
  • Key capabilities:
    • Prompt Design – Interactive prompt editor with system instructions, few-shot examples, and parameter tuning (temperature, top-k, top-p)
    • Prompt Gallery – Pre-built prompt templates for common use cases
    • Prompt Optimizer – Automated prompt refinement using Zero-Shot and Data-Driven modes (GA 2025)
    • Model Tuning – Fine-tune models with custom datasets without managing infrastructure
    • Model Distillation – Train smaller, faster student models from larger teacher models
    • Multimodal Testing – Test text, image, video, and audio inputs/outputs
  • Tuning methods supported:
    • Supervised Fine-Tuning (SFT) – Train on labeled input-output pairs
    • Reinforcement Learning from Human Feedback (RLHF) – Align model outputs with human preferences
    • Distillation – Transfer knowledge from large teacher to smaller student model
    • LoRA (Low-Rank Adaptation) – Parameter-efficient fine-tuning
  • Supports Gemini, Imagen, and Codey model families for tuning.
  • Provides code export to Python SDK for production integration.

Model Training

  • Vertex AI provides two primary training approaches:
    • AutoML – No-code training for tabular, image, video, and text data with automatic feature engineering, architecture search, and hyperparameter tuning
    • Custom Training – Full control using any ML framework (TensorFlow, PyTorch, XGBoost, scikit-learn, JAX) with custom containers or prebuilt containers
  • AutoML capabilities:
    • AutoML Tabular – Classification, regression, forecasting
    • AutoML Image – Classification, object detection, segmentation
    • AutoML Video – Classification, object tracking, action recognition
    • AutoML Text – Classification, entity extraction, sentiment analysis
  • Custom Training features:
    • Supports NVIDIA GPUs: A100, H100, A3 (H100 80GB), and A4X (GB200 NVL72 rack-scale, 2026)
    • Supports Cloud TPUs: TPU v2, v3, v5e, v5p for large-scale training
    • Distributed training across multiple nodes with automatic orchestration
    • Prebuilt containers for TensorFlow, PyTorch, XGBoost, scikit-learn
    • Custom containers for any framework or dependency
    • Hyperparameter tuning service (Vizier-based)
  • Vertex AI Training with Cluster Director (2025) – Fully managed, resilient Slurm environment for large-scale training workloads, simplifying multi-node GPU/TPU training.
  • Training is serverless – no infrastructure provisioning; billed per compute-hour in 30-second increments.
  • No charges if training fails (except user-initiated cancellations).

Model Deployment

  • Vertex AI supports multiple deployment options for serving predictions:
    • Online Prediction – Low-latency, real-time predictions via HTTPS endpoints
    • Batch Prediction – High-throughput predictions on large datasets (asynchronous)
    • Private Endpoints – Deploy within VPC for network isolation
  • Online Prediction features:
    • Dedicated endpoints with configurable machine types and accelerators (GPUs, TPUs)
    • Automatic scaling (min/max replicas) based on traffic
    • Traffic splitting for A/B testing and canary deployments
    • Model versioning – deploy multiple model versions to the same endpoint
    • Prebuilt containers for TensorFlow, PyTorch, XGBoost, scikit-learn serving
    • Custom serving containers for any framework
    • TPU VM deployment for high-throughput inference
  • Batch Prediction features:
    • Process large datasets from Cloud Storage or BigQuery
    • Serverless – no persistent infrastructure
    • Cost-effective for non-real-time workloads
    • Supports the same model formats as online prediction
  • Foundation models (Gemini, Claude, Llama) are served via managed APIs without requiring endpoint deployment.
  • Supports model explainability (feature attributions) on deployed endpoints.

Vertex AI Pipelines

  • Vertex AI Pipelines is a serverless orchestration service for automating, monitoring, and governing ML workflows.
  • Supports two pipeline frameworks:
    • Kubeflow Pipelines (KFP) – Python-based pipeline SDK for defining ML workflows as directed acyclic graphs (DAGs)
    • TensorFlow Extended (TFX) – End-to-end TensorFlow production ML pipelines
  • Key features:
    • Serverless execution – no cluster management required
    • Pipeline scheduling and triggering (event-based or cron)
    • Artifact and metadata tracking for lineage
    • Integration with Vertex AI services (training, endpoints, Feature Store, Model Registry)
    • Reusable pipeline components and templates
    • Pipeline versioning and caching for faster iteration
  • Common pipeline patterns:
    • Data ingestion → Feature engineering → Training → Evaluation → Deployment
    • Continuous training pipelines triggered by data drift or schedule
    • A/B testing pipelines for comparing model versions
  • Pipelines integrate with Cloud Logging, Cloud Monitoring, and IAM for governance.

Vertex AI Feature Store

  • Vertex AI Feature Store is a fully managed service for organizing, storing, and serving ML features at scale.
  • Built on BigQuery – feature data is managed within BigQuery tables or views, eliminating the need for a separate offline store.
  • Key capabilities:
    • Feature Management – Centralized registry of features with metadata, lineage, and versioning
    • Online Serving – Low-latency feature retrieval for real-time predictions (single-digit millisecond latency)
    • Offline Serving – Point-in-time correct feature retrieval for training data generation
    • Feature Sharing – Share features across teams and projects to reduce duplication
    • Embedding Support – Store and serve vector embeddings for GenAI/RAG applications
    • Vector Similarity Search – Perform approximate nearest neighbor (ANN) search on stored embeddings
  • Integrated with Dataplex Universal Catalog for feature metadata tracking and discovery.
  • Supports streaming and batch feature ingestion from BigQuery, Cloud Storage, and Dataflow.
  • Feature Store integrates with Vertex AI RAG Engine for retrieval-augmented generation workflows.

Vertex AI Vector Search

  • Vertex AI Vector Search (formerly Matching Engine) is a high-performance, fully managed vector similarity search service.
  • Built on Google’s ScaNN (Scalable Nearest Neighbors) algorithm for efficient approximate nearest neighbor search.
  • Key features:
    • Supports billions of vectors with low-latency retrieval
    • Real-time index updates (streaming inserts)
    • Filtering with boolean and numeric predicates
    • Multiple distance metrics: cosine, dot product, Euclidean
    • Hybrid search combining vector similarity with metadata filters
  • Vector Search 2.0 (2025-2026):
    • Storage-optimized tier – Cost-effective solution for large-scale RAG and semantic search applications
    • Auto-tuning – Eliminates manual index configuration, automatically optimizes for workload
    • Integrated data retrieval – Returns full item data (not just IDs), eliminating the need for a separate key-value store
  • Use cases: semantic search, recommendation systems, RAG, image/video retrieval, anomaly detection.
  • Integrates with Vertex AI Feature Store for embedding storage and serving.

Vertex AI Agent Builder

  • Vertex AI Agent Builder is Google Cloud’s comprehensive platform for building, scaling, and governing AI agents.
  • Evolved from and consolidates: Dialogflow CX (now Conversational Agents), Vertex AI Search, and Generative AI App Builder.
  • Key pillars:
    • Agent Development Kit (ADK) – Code-first Python framework for building multi-agent systems
    • Agent Studio – Low-code visual builder with 35+ pre-built agent templates
    • Agent Engine – Managed runtime for deploying and scaling agents in production
    • Persistent Memory – Long-term memory and session management for agents
    • Enterprise Governance – Access controls, audit logging, and compliance
  • Conversational Agents (formerly Dialogflow CX):
    • Build hybrid agents combining deterministic flows with generative AI
    • Playbooks – Define goals and tools, let the LLM determine execution paths
    • Multi-channel support: web, phone, messaging platforms
    • Phone Gateway for voice agents
  • Vertex AI Search:
    • Enterprise search over structured, unstructured, and website data
    • RAG-powered answers grounded in enterprise documents
    • Supports PDF, HTML, Cloud Storage, BigQuery, and third-party data sources
  • Supports Agent-to-Agent (A2A) protocol for multi-agent collaboration.
  • 200+ foundation models accessible within Agent Builder workflows.

Grounding

  • Grounding connects generative AI model outputs to verifiable sources of information, reducing hallucinations and improving accuracy.
  • Grounding options:
    • Grounding with Google Search – Connects models to real-time, publicly available web content with cited sources
    • Grounding with Vertex AI Search – Connects models to enterprise data (documents, databases, websites)
    • Grounding with Parallel Web Search – Multi-hop agents that perform deeper web searches for complex questions
  • Key features:
    • Dynamic Retrieval – Model automatically determines when grounding is needed based on the prompt
    • Source Citations – Responses include links to source documents
    • Grounding Scores – Confidence scores indicating how well the response is supported by sources
    • Supports up to 10 Vertex AI Search data sources per request
    • Can combine Google Search grounding with enterprise data grounding
    • Limit of 1 million queries per day for Grounding with Google Search
  • Starting with Gemini 2.0, Google Search is available as a tool – the model can autonomously decide when to search.
  • Gemini 3 Pro includes 5,000 free Google Search grounding queries per month.

Model Evaluation

  • Vertex AI provides comprehensive model evaluation capabilities for both traditional ML and generative AI models.
  • Traditional ML Evaluation:
    • Classification metrics: accuracy, precision, recall, F1, AUC-ROC, confusion matrix
    • Regression metrics: MAE, RMSE, R-squared
    • Feature importance and attribution analysis
    • Slice-based evaluation for fairness assessment
  • Generative AI Evaluation:
    • AutoSxS (Auto Side-by-Side) – Automated pairwise comparison between models
    • Pointwise evaluation metrics: fluency, coherence, safety, groundedness
    • Custom evaluation criteria using LLM-as-a-judge
    • RAG evaluation: context relevance, answer faithfulness, answer relevance
    • Batch evaluation across datasets
  • Evaluation results stored in Vertex AI Experiments for tracking and comparison.
  • Supports evaluation of fine-tuned models against base models to measure improvement.

Vertex AI Workbench

  • Vertex AI Workbench provides managed Jupyter notebook environments for ML development and experimentation.
  • Workbench Instances (managed notebooks):
    • JupyterLab-based environment with pre-installed ML frameworks
    • Configurable machine types with GPU/TPU accelerators
    • Automatic idle shutdown to reduce costs
    • Integration with Git for version control
    • Pre-authenticated access to Google Cloud services
    • Supports custom containers for specialized environments
  • Colab Enterprise – Collaborative notebook environment integrated with Vertex AI:
    • Serverless and managed runtimes
    • Enterprise security (VPC-SC, CMEK, IAM)
    • Shared notebooks with team collaboration features
    • Code completion with Gemini Code Assist
  • Notebooks can directly launch Vertex AI training jobs, access Feature Store, and deploy models.

MLOps

  • Vertex AI provides integrated MLOps tooling for production ML lifecycle management.
  • Model Registry:
    • Central repository for all trained models
    • Model versioning with version aliases and descriptions
    • Model lineage tracking (training data, pipeline, hyperparameters)
    • Model labels and metadata for organization
    • Import models from any source (Vertex AI, BigQuery ML, external)
    • Default version management for deployments
  • Vertex AI Experiments:
    • Track and compare training runs with metrics, parameters, and artifacts
    • Automatic logging integration with TensorFlow and PyTorch
    • Visualization of experiment results
    • Integration with Vertex AI Pipelines for automated experimentation
  • Model Monitoring:
    • Detect data drift (training-serving skew) automatically
    • Feature distribution monitoring
    • Prediction output monitoring for quality degradation
    • Configurable alerting thresholds
    • Integration with Cloud Monitoring and Cloud Logging
  • Vertex AI Metadata:
    • Track artifacts, executions, and their lineage across the ML lifecycle
    • Query metadata for auditing and compliance
    • Automatic capture from Vertex AI Pipelines

Pricing Overview

  • Vertex AI uses a pay-as-you-go pricing model with no upfront commitments.
  • Generative AI / Foundation Models:
    • Priced per million tokens (input and output separately)
    • Gemini 2.5 Flash: ~$0.15/1M input tokens, ~$0.60/1M output tokens (cost-efficient)
    • Gemini 2.5 Pro: ~$1.25/1M input tokens, ~$5.00/1M output tokens
    • Third-party models (Claude, Llama): pricing varies by model
    • Grounding with Google Search: $2.50 per 1,000 requests (Gemini 3 Pro includes 5,000 free/month)
  • Custom Training:
    • Charged per node-hour based on machine type and accelerators
    • 30-second billing increments (no minimum duration)
    • No charge for failed training jobs (except user cancellations)
    • GPU pricing varies: A100 40GB ~$3.67/hr, H100 80GB ~$11.54/hr per accelerator
    • TPU pricing: v5e ~$1.20/hr per chip
  • Predictions:
    • Online prediction: charged per node-hour for deployed endpoint compute
    • Batch prediction: charged per node-hour for processing time
    • Automatic scaling to zero available (no charge when idle)
  • Other Services:
    • AutoML Training: per node-hour (varies by data type)
    • Feature Store: per GB stored + per million online reads
    • Vector Search: per node-hour for deployed indexes + storage
    • Pipelines: per pipeline run based on compute consumed
    • Agent Builder: per query/session based on usage
  • Total monthly costs range from under $100 for prototyping to $100,000+ for enterprise production workloads.
  • Free tier: new customers receive $300 Google Cloud credits applicable to Vertex AI services.

Vertex AI vs AWS SageMaker vs Azure ML

Feature Google Cloud Vertex AI AWS SageMaker Azure ML
Platform Philosophy Unified, integrated with BigQuery and Google AI research Broadest feature set, deep AWS integration Enterprise governance, Microsoft ecosystem integration
Foundation Models 200+ models (Gemini, Claude, Llama) via Model Garden 100+ models via Amazon Bedrock (Titan, Claude, Llama, Mistral) OpenAI models (GPT-4, o1) + open models via Azure AI Foundry
AutoML AutoML for tabular, image, video, text SageMaker Autopilot (tabular focus) Automated ML for tabular, vision, NLP
Custom Training GPUs + TPUs, serverless, Cluster Director GPUs + Trainium/Inferentia, SageMaker Training GPUs, Azure ML Compute clusters
Notebooks Workbench + Colab Enterprise SageMaker Studio Notebooks Azure ML Notebooks
Pipelines Kubeflow/TFX-based, serverless SageMaker Pipelines (proprietary SDK) Azure ML Pipelines (designer + SDK)
Feature Store BigQuery-integrated, GenAI-ready SageMaker Feature Store Azure ML Feature Store (managed)
Vector Search Vertex AI Vector Search (ScaNN) OpenSearch Serverless / Bedrock Knowledge Bases Azure AI Search (vector + hybrid)
AI Agents Agent Builder (ADK, Agent Engine, Studio) Amazon Bedrock Agents Azure AI Agent Service
Grounding/RAG Google Search + Vertex AI Search grounding Bedrock Knowledge Bases + Guardrails Azure AI Search + On Your Data
MLOps Model Registry, Experiments, Monitoring, Metadata Model Registry, Experiments, Model Monitor, Clarify Model Registry, Managed Endpoints, Responsible AI
Pricing Model Pay-per-use, 30s increments, TPU cost advantage Pay-per-use, instance markups 15-40% over EC2 Pay-per-use, lower platform surcharges
Key Differentiator BigQuery integration, TPU access, Google AI research Broadest service catalog, largest ecosystem OpenAI partnership, Power BI/Office integration
  • Choose Vertex AI when: using BigQuery for data, need TPU training, prefer Google’s Gemini models, want tight integration with Google Cloud services.
  • Choose SageMaker when: already on AWS, need the broadest feature set, require deep integration with AWS services, want Trainium/Inferentia for cost-effective inference.
  • Choose Azure ML when: using Microsoft ecosystem (Office, Teams, Power BI), need OpenAI GPT-4/o1 models, have enterprise governance requirements via Azure AD.

GCP Certification Exam Practice Questions

  • Questions are collected from Internet and the answers are marked as per my knowledge and understanding (which might differ with yours).
  • GCP services are updated everyday and both the answers and questions might be outdated soon, so research accordingly.
  • GCP exam questions are not updated to keep up the pace with GCP updates, so even if the underlying feature has changed the question might not be updated
  • Open to further feedback, discussion and correction.
  1. Your data science team needs to quickly build a classification model on structured data without writing custom code. They want Google Cloud to handle feature engineering and architecture selection. Which Vertex AI service should they use?
    1. Vertex AI Custom Training with prebuilt containers
    2. Vertex AI AutoML Tabular
    3. Vertex AI Workbench with scikit-learn
    4. Vertex AI Pipelines with Kubeflow
  2. A company wants to deploy a Gemini model that provides answers grounded in their internal product documentation stored in Cloud Storage. Which Vertex AI feature should they use?
    1. Grounding with Google Search
    2. Grounding with Vertex AI Search
    3. Model distillation
    4. Vertex AI Feature Store
  3. Your organization needs to orchestrate an ML workflow that includes data preprocessing, model training, evaluation, and conditional deployment. The solution should be serverless and track artifacts automatically. What should you use?
    1. Cloud Composer (Apache Airflow)
    2. Cloud Run Jobs
    3. Vertex AI Pipelines with Kubeflow Pipelines SDK
    4. Cloud Scheduler with Cloud Functions
  4. A team needs to serve features with single-digit millisecond latency for real-time fraud detection. They already have feature data in BigQuery. Which service should they use?
    1. BigQuery real-time queries
    2. Memorystore for Redis
    3. Vertex AI Feature Store with optimized online serving
    4. Vertex AI Vector Search
  5. Your company wants to build a semantic search application that finds similar products from a catalog of 10 million items using embedding vectors. Which Vertex AI service is best suited?
    1. Vertex AI Feature Store
    2. BigQuery vector functions
    3. Vertex AI Search
    4. Vertex AI Vector Search
  6. An ML engineer wants to fine-tune a Gemini model to follow their company’s specific writing style using a small dataset of 500 examples. Which tuning approach provides the most parameter-efficient method?
    1. Full supervised fine-tuning
    2. RLHF
    3. LoRA (Low-Rank Adaptation)
    4. Model distillation
  7. A team has deployed a model on a Vertex AI endpoint and notices prediction quality degrading over time. Which Vertex AI capability should they enable to detect this automatically?
    1. Vertex AI Experiments
    2. Vertex AI Pipelines
    3. Vertex AI Metadata
    4. Vertex AI Model Monitoring (training-serving skew detection)
  8. Your team needs to deploy multiple versions of a model and gradually shift traffic from the old version to the new version. Which Vertex AI deployment feature enables this?
    1. Batch prediction
    2. Private endpoints
    3. Traffic splitting on online prediction endpoints
    4. Model Registry versioning
  9. A startup wants to use Claude (Anthropic) models on Google Cloud with enterprise security features like VPC Service Controls and data residency. Where should they access Claude?
    1. Directly from Anthropic’s API
    2. Vertex AI Model Garden (managed API)
    3. Deploy Claude on GKE
    4. Cloud Run with Claude container
  10. Your organization is training a large language model and needs to use TPU v5e pods for distributed training on Vertex AI. Which training method should they use?
    1. AutoML
    2. Custom Training with TPU VM configuration
    3. Vertex AI Studio fine-tuning
    4. BigQuery ML
  11. A company wants to build a customer service chatbot that combines deterministic conversation flows for order tracking with generative AI for open-ended questions. Which service should they use?
    1. Vertex AI Studio
    2. Vertex AI Search
    3. Conversational Agents (formerly Dialogflow CX) with Generative Playbooks
    4. Cloud Functions with Gemini API
  12. Which of the following is NOT a component of Vertex AI Agent Builder? (Select one)
    1. Agent Development Kit (ADK)
    2. Agent Engine
    3. Agent Studio
    4. Agent Trainer

References

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