Generative AI in Production (GC-GAIP)

Traditional MLOps is a set of practices to productionize traditional ML systems for enterprise applications. Generative AI raises new challenges in managing and productionizing applications at scale. The field of generative AI operations seeks to address these new challenges. In this course, you learn about the challenges that arise when deploying and productionizing generative AI-powered applications. You learn how to secure your generative AI-powered applications. Finally, you will discuss best practices for logging and monitoring your generative AI-powered applications in production.


What you'll learn

  • Understand the challenges in productionizing applications using generative AI
  • Manage experimentation and evaluation for LLM-powered application
  • Productionize LLM-powered applications
  • Secure generative AI applications
  • Implement logging and monitoring for LLM-powered applications


Target Audience

  • Developers, DevOps engineers and machine learning engineers who wish to operationalize GenAI-based applications


Prerequisites

  • Completion of the "Application Development with LLMs on Google Cloud" or equivalent knowledge.


Products

  • Vertex AI
Show details


Modules

Module 01: 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 02: 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 03: 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 04: 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
Download With Schedule Download Without Schedule