AI-Native TrainingModule 4
Learning Objectives
Design, build, and productionize ML models
Implement MLOps practices for generative AI
Deploy and maintain AI systems at scale
Optimize ML systems for performance and cost
Topics Covered
1
Introduction to AI/ML on Cloud
The AI/ML ecosystem and project lifecycle
Vertex AI: Unified ML platform
BigQuery ML: SQL-based ML
AutoML: No-code ML solutions
ML project lifecycle phases
2
Data Preparation and Feature Engineering
Building robust data pipelines
Data pipeline design with cloud services
Feature selection and extraction
Feature scaling and normalization
Feature store management
3
Model Training and Evaluation
Training approaches and evaluation metrics
Custom training with TensorFlow/PyTorch
AutoML for automated model selection
Transfer learning and fine-tuning
Evaluation metrics for different model types
4
Machine Learning Operations (MLOps)
CI/CD for ML and continuous monitoring
MLOps principles: CI/CT/CD/CM
Version control: code, data, models
Automated testing and validation pipelines
Monitoring: drift detection, alerts, incident response
5
Building and Deploying on Cloud
Production deployment and scaling
Deployment options: online, batch, edge
Autoscaling and load balancing
A/B testing and canary deployments
Disaster recovery planning
Hands-On Projects
End-to-End ML Pipeline
intermediate4 hours
Build complete ML pipeline on cloud platform
CI/CD for ML
advanced3 hours
Implement CI/CD pipeline for ML model
Production Deployment
advanced3 hours
Deploy model with monitoring and alerting
Recommended Resources
Google Cloud ML Training
External learning resources to supplement your training.
Assessment
Design ML system architecture + Implement MLOps workflow + Deploy production model