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Generative AI in Production
Course 1485
1 DAY COURSE

Course Outline

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.

Generative AI in Production Benefits

  • This course will empower you to:

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

  • Prerequisites

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

    Who Should Attend

    Developers and DevOps Engineers who wish to operationalize GenAI-based applications

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
  • Define and compare RAG and ReAct

Module 2: Generative AI Application Deployment

  • Evaluate application deployment options
  • Deploy, package, and version apps

Module 3: Productionizing Generative AI

  • Maintain and update LLM models
  • Test and evaluate gen AI-powered apps
  • Deploy CI/CD pipelines for gen AI-powered apps

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.

Module 4: Securing Generative AI Applications

  • Identify security challenges for gen AI applications
  • Understand prompt security issues
  • Apply sensitive data protection and DLP API
  • Implement Model Armor

 

Module 5: Observability for Production LLM Systems

  • 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
Course Dates
Attendance Method
Note about the Certification Exam

When you register for the course, you will be prompted to choose Y/N to take the exam. Please select yes, as all HHS CISO employees are required to attempt the exam if one is offered for the course. Please be advised, if your course if funded by DIR, the Certification Organization has agreed to provide DIR the pass/fail status of your exam. DIR will only share this information in an aggregated report to state leadership that reflects total exam pass or fails. No individual names of any students will be included in any reports.

DIR requires that you submit the request for your exam voucher within one month of the last day of your course. DIR requires that you take your exam within six months of the last day of your course.

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