Generative AI in Production

In this course, you learn about the different challenges that arise when productionizing generative AI-powered applications versus traditional ML. You will learn how to manage experimentation and tuning of your LLMs, then you will discuss how to deploy, test, and maintain your LLM-powered applications. Finally, you will discuss best practices for logging and monitoring your LLM-powered applications in production.

Generative AI in Production Benefits

• This course will empower you to:

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

• Prerequisites

  Completion of "Introduction to Developer Efficiency on Google Cloud" or equivalent knowledge.

Generative AI in Production Course Outline

Learning Objectives

Module 1: Introduction to Generative AI in Production

• Understand generative AI operations
• Compare traditional MLOps and GenAIOps
• Analyze the components of an LLM system

Module 2: Managing Experimentation

• Experiment with datasets and prompt engineering.
• Utilize RAG and ReACT architecture.
• Evaluate LLM models. • Track experiments.

Module 3: Productionizing Generative AI

• Deploy, package, and version models
• Test LLM systems
• Maintain and update LLM models
• Manage prompt security and migration

Module 4: Logging and Monitoring for Production LLM Systems

• Utilize Cloud Logging
• Version, evaluate, and generalize prompts
• Monitor for evaluation-serving skew
• Utilize continuous validation.