Detailed Course Outline
Module 1 - Introduction to Generative AI in Production
Topics:
- Generative AI Operations
- Traditional MLOps vs. GenAIOps
- Components of an LLM System
- RAG/ReAct architecture
Objectives:
- Understand generative AI operations
- Compare traditional MLOps and GenAIOps
- Analyze the components of an LLM system
- Define and compare RAG and ReAct
Module 2 - Generative AI Application Deployment
Topics:
- Application deployment options
- Deployment, packaging, and versioning
Objectives:
- Evaluate application deployment options
- Deploy, package, and version apps
Activities:
- Lab: Deploying an Agentic Application on Cloud Run
Module 3 - Productionizing Generative AI
Topics:
- Maintenance and updates
- Testing and evaluation
- CI/CD pipelines for gen AI-powered apps
Objectives:
- Maintain and update LLM models
- Test and evaluate gen AI-powered apps
- Deploy CI/CD pipelines for gen AI-powered apps
Activities:
- Lab: Tracking Versions of Generative AI Applications
Module 4 - Securing Generative AI Applications
Topics:
- Security challenges
- Prompt security
- Sensitive Data Protection and DLP API
- Model Armor
Objectives:
- Identify security challenges for gen AI applications
- Understand prompt security issues
- Apply sensitive data protection and DLP API
- Implement Model Armor
Activities:
- Lab: Securing Generative AI-Powered Applications
Module 5 - Observability for Production LLM Systems
Topics:
- Cloud Operations
- Cloud Logging
- Monitoring
- Cloud Trace
- Agent Analytics and AgentOps
- Putting it all together
Objectives:
- Describe the purpose and capabilities of Google Cloud Observability
- Explain the purpose of Cloud Monitoring
- Explain the purpose of Cloud Logging
- Explain the purpose of Cloud Trace
Activities:
- Lab: Logging, Monitoring, and Agent Analytics