AvinashAI.com

Master Google Vertex AI

Enterprise-grade AI/ML platform mastery for cloud professionals

Complete Vertex AI Learning Path

Designed for cloud infrastructure professionals with AI/GenAI foundations. Master Google's enterprise AI platform through hands-on labs and real-world scenarios.

1

Vertex AI Platform Foundation

⏱️ 3-4 hours

🎯 Learning Objectives

  • Navigate Vertex AI architecture and ecosystem
  • Configure IAM roles and security policies
  • Understand pricing and cost optimization
  • Set up development environment

🧪 Hands-on Lab

Set up your first Vertex AI project with proper IAM configuration and explore the console interface

📖 Essential Resources

📘 Official Documentation 🎓 Google Cloud Skills Boost 📝 DataCamp Tutorial

Prerequisites:

  • Active Google Cloud account with billing enabled
  • Basic understanding of cloud computing concepts
  • Familiarity with GCP console navigation
2

AutoML & Custom Training

⏱️ 6-8 hours

🎯 Learning Objectives

  • Build AutoML models for images, text, and tabular data
  • Train custom TensorFlow and PyTorch models
  • Implement hyperparameter tuning strategies
  • Compare AutoML vs custom training approaches

🧪 Hands-on Labs

Create AutoML image classifier, train custom model with distributed training, and implement hyperparameter optimization

📖 Essential Resources

🚀 AutoML Beginner's Guide ⚙️ Custom Training Overview 💻 Official Code Samples 📚 Tutorials Collection

Enterprise Applications:

  • Document classification for compliance
  • Quality control in manufacturing
  • Predictive maintenance for infrastructure
  • Customer churn prediction
3

Generative AI & Foundation Models

⏱️ 5-6 hours

🎯 Learning Objectives

  • Work with PaLM, Gemini, and Codey models
  • Master prompt engineering and fine-tuning
  • Implement RAG (Retrieval Augmented Generation)
  • Build production-ready GenAI applications

🧪 Hands-on Labs

Build intelligent chatbot with Gemini, create code generation tool, implement document Q&A with RAG

📖 Essential Resources

🎨 Vertex AI Studio Course ⚡ Multimodal Quickstart 📋 Model Reference

Production Use Cases:

  • Automated technical documentation
  • Code review and optimization assistant
  • Customer support automation
  • Content personalization at enterprise scale
4

Model Deployment & Serving

⏱️ 4-5 hours

🎯 Learning Objectives

  • Deploy models to managed endpoints
  • Configure auto-scaling and load balancing
  • Implement A/B testing for model versions
  • Set up batch prediction pipelines

🧪 Hands-on Labs

Deploy scalable endpoint, configure traffic splitting, create batch prediction job, implement canary deployment

📖 Essential Resources

🚀 Deployment Guide 📦 Batch Predictions ⚡ Online Predictions

Production Considerations:

  • Latency optimization and caching strategies
  • Cost management for high-traffic applications
  • Multi-region deployment for availability
  • Security and access control implementation
5

MLOps & Pipeline Automation

⏱️ 6-8 hours

🎯 Learning Objectives

  • Build ML pipelines with Vertex Pipelines
  • Implement CI/CD for ML workflows
  • Set up model monitoring and drift detection
  • Automate retraining and deployment

🧪 Hands-on Labs

Create end-to-end ML pipeline, implement automated retraining, set up model monitoring dashboard

📖 Essential Resources

🔄 Vertex Pipelines Intro 📊 Model Monitoring 🏗️ Pipeline Building Guide ⚙️ Kubeflow Pipelines v2

Enterprise MLOps Patterns:

  • Integration with GitHub Actions and Cloud Build
  • Model versioning and governance frameworks
  • Automated testing for ML models
  • Compliance and audit trails for regulated industries
6

Enterprise Integration & APIs

⏱️ 4-5 hours

🎯 Learning Objectives

  • Integrate Vertex AI with existing systems
  • Master Vertex AI SDK and REST APIs
  • Implement authentication and security
  • Build scalable ML-powered microservices

🧪 Hands-on Labs

Build REST API wrapper, integrate with Kubernetes, create ML microservice architecture

📖 Essential Resources

📘 API Reference 🐍 Python SDK Guide 💻 Integration Samples

Integration Architecture:

  • Event-driven ML with Pub/Sub and Cloud Functions
  • Container-based deployments with GKE
  • Multi-cloud and hybrid cloud strategies
  • Enterprise security and compliance patterns