Google Vertex AI Platform Training

Master Google’s Unified AI Platform for Enterprise

Build Production Multi-Modal AI Systems on Google Cloud

Learn to leverage Vertex AI’s comprehensive platform for building, deploying, and managing AI at scale. From Gemini 2 Ultra to custom models, master Google’s enterprise AI ecosystem.

🎯 Course Overview

This intensive 3-day course covers Vertex AI’s complete platform including the latest 2025 features: unified orchestration, multi-modal pipelines, and enterprise-grade MLOps. Build production systems using Google’s most advanced models.

What You’ll Master

• 🚀 Unified Platform: End-to-end ML lifecycle management
• 🤖 Latest Models: Gemini 2 Ultra, PaLM 3, and open-source integration
• 🎯 Multi-Modal AI: Vision, text, audio, and video pipelines
• 📊 Enterprise Integration: BigQuery, Workspace, and GCP services
• 🔒 Compliance & Security: Bias detection, monitoring, and governance

Who Should Attend

• ML Engineers moving to Google Cloud
• Data Scientists building production models
• Cloud Architects designing AI infrastructure
• DevOps teams managing ML pipelines
• Enterprise teams evaluating Vertex AI

📚 Detailed Curriculum

Day 1: Vertex AI Foundations & Model Development

Morning Session: Platform Overview & Setup

• Vertex AI Architecture

  • Unified platform benefits
  • Component overview: Training, Prediction, Pipelines
  • Integration with GCP ecosystem
  • Pricing and resource optimization

• Model Catalog Deep Dive

  • Gemini 2 Ultra capabilities
  • PaLM 3 for enterprise use cases
  • Open-source model integration
  • Model selection criteria

• Hands-On Lab 1: Environment Setup & First Model

  • Configure Vertex AI workspace
  • Deploy pre-trained model
  • Test inference endpoints
  • Monitor usage and costs

Afternoon Session: Custom Model Development

• Training Infrastructure

  • Managed training jobs
  • Distributed training setup
  • GPU/TPU optimization
  • Hyperparameter tuning

• AutoML Capabilities

  • Tabular, vision, text, and video
  • Data preparation requirements
  • Training configuration
  • Model evaluation metrics

• Hands-On Lab 2: Train Custom Models

  • Prepare training dataset
  • Configure AutoML pipeline
  • Launch distributed training
  • Evaluate model performance

Day 2: Multi-Modal AI & Advanced Features

Morning Session: Multi-Modal Pipeline Development

• Gemini 2 Ultra Integration

  • Multi-modal understanding
  • Vision + text applications
  • Audio processing capabilities
  • Real-time streaming

• Pipeline Orchestration

  • Vertex AI Pipelines design
  • Component development
  • Workflow automation
  • Pipeline versioning

• Hands-On Lab 3: Build Multi-Modal Application

  • Create vision + text pipeline
  • Implement Gemini 2 Ultra
  • Add audio processing
  • Deploy streaming endpoint

Afternoon Session: RAG & Vector Search

• Vertex AI Search Integration

  • Document ingestion pipelines
  • Embedding generation
  • Vector database setup
  • Semantic search optimization

• RAG Implementation

  • Knowledge base creation
  • Retrieval strategies
  • Response generation
  • Citation management

• Hands-On Lab 4: Production RAG System

  • Ingest enterprise documents
  • Build vector search index
  • Implement RAG pipeline
  • Add access controls

Day 3: Enterprise Integration & MLOps

Morning Session: Enterprise Platform Integration

• BigQuery ML Integration

  • Direct model training in BigQuery
  • Feature engineering at scale
  • Real-time predictions
  • Cost optimization

• Google Workspace Integration

  • Document AI capabilities
  • Apps Script automation
  • Security and compliance
  • User authentication

• Hands-On Lab 5: Enterprise Data Pipeline

  • Connect BigQuery datasets
  • Build feature store
  • Implement batch predictions
  • Create monitoring dashboard

Afternoon Session: MLOps & Production Deployment

• Model Monitoring & Management

  • Drift detection setup
  • Bias monitoring
  • Performance tracking
  • Automated retraining

• Production Best Practices

  • A/B testing frameworks
  • Canary deployments
  • Rollback strategies
  • SLA management

• Hands-On Lab 6: Complete MLOps Pipeline

  • Deploy model with monitoring
  • Set up drift detection
  • Implement A/B testing
  • Configure auto-scaling

🛠️ Real-World Projects

Project 1: Customer Intelligence Platform

Build an enterprise system that:

• Analyzes customer interactions across channels
• Predicts churn and lifetime value
• Generates personalized recommendations
• Integrates with CRM systems

Project 2: Document Processing Pipeline

Create an intelligent system that:

• Processes multi-format documents
• Extracts structured data
• Validates against business rules
• Feeds downstream systems

Project 3: Real-Time Analytics Engine

Develop a platform that:

• Processes streaming data
• Detects anomalies in real-time
• Triggers automated responses
• Provides executive dashboards

💡 Advanced Topics Covered

Model Optimization

• Quantization strategies
• Edge deployment with Vertex AI Edge
• Latency optimization techniques
• Cost-performance trade-offs

Security & Compliance

• VPC Service Controls
• Customer-managed encryption keys
• Data residency requirements
• Audit logging configuration

Advanced Architectures

• Multi-region deployments
• Hybrid cloud patterns
• Federated learning setup
• Private model endpoints

Integration Patterns

• Event-driven architectures
• Pub/Sub integration
• Cloud Functions triggers
• Dataflow pipelines

📋 Prerequisites

Required Knowledge

• Python programming (intermediate)
• Basic machine learning concepts
• Cloud computing fundamentals
• SQL basics (for BigQuery)

Recommended Experience

• GCP basics (helpful but not required)
• Docker containers (beneficial)
• REST APIs (useful)

Technical Requirements

• Laptop with Chrome browser
• Google Cloud account (we provide credits)
• Python development environment
• GitHub account

💰 Pricing & Options

Training Formats

On-Site Training

• Price: $15,000 for up to 12 participants
• Duration: 3 consecutive days
• Includes: Customized use cases and datasets
• Bonus: GCP architecture review session

Virtual Training

• Price: $10,000 for up to 12 participants
• Duration: 3 days (6 hours per day)
• Format: Live online with hands-on labs
• Support: 30-day post-training access

Public Classes

• Price: $1,995 per participant
• Schedule: Monthly sessions
• Locations: Major tech hubs + online
• Next Date: View Schedule

What’s Included

• Comprehensive lab guide (400+ pages)
• $300 GCP credits per participant
• Production-ready templates
• Vertex AI best practices guide
• Certificate of completion
• Alumni community access
• Quarterly update sessions

🎯 Learning Outcomes

Upon completion, you will be able to:

✅ Design end-to-end AI solutions on Vertex AI
✅ Build multi-modal applications with Gemini 2
✅ Implement production MLOps pipelines
✅ Integrate with BigQuery and GCP services
✅ Deploy models with monitoring and governance
✅ Optimize for cost and performance
✅ Handle enterprise security requirements
✅ Scale AI across your organization

👨‍🏫 Expert Instructors

Learn from Google Cloud certified professionals:

• GCP expertise: Multiple certifications and real deployments
• Production experience: Built Vertex AI systems at scale
• Industry knowledge: Vertical-specific implementations
• Continuous updates: Direct access to Google’s latest features

🚀 Enroll Now

Accelerate Your AI Journey with Google Cloud

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📚 Learning Resources

Pre-Course Materials

• Vertex AI Quick Start Guide
• GCP AI Services Overview
• Gemini 2 Capabilities Guide
• Cost Optimization Checklist

Continued Learning

• Monthly office hours with instructors
• Access to private Slack workspace
• Beta feature early access program
• Annual alumni conference

Complementary Training

• BigQuery for Data Scientists
• GCP Security for AI
• Kubernetes on GCP

❓ Frequently Asked Questions

Q: Do I need prior GCP experience?
A: Basic cloud knowledge helps, but we cover all necessary GCP concepts. The focus is on Vertex AI specifically.

Q: How does this compare to AWS SageMaker?
A: We highlight key differences and can help you map concepts if you’re coming from AWS.

Q: Are the labs on real GCP?
A: Yes! You’ll work in actual GCP projects with real Vertex AI resources.

Q: What about costs after training?
A: We teach cost optimization throughout and provide calculators for budgeting your projects.

Q: Can we focus on our industry?
A: On-site training can be customized with industry-specific examples and use cases.

More FAQs →

🏆 Success Stories

🎓 Certification & Recognition

Graduates receive:

• Official training certificate
• Digital credential badge
• LinkedIn skill verification
• Portfolio project listing

Optional Google Cloud certification prep:

• Professional ML Engineer exam guidance
• Practice questions and scenarios
• Study group access