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Generative AI in Production

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.
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Course Overview

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.
  • Scroll right to view Objectives, Prerequisites, and Agenda

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

Before attending this course, students should have attended the Application Development with LLMs on Google Cloud course

  • Introduction to GenAI in Production
  • GenAI Application Deployment
  • Productionizing GenAI
  • Securing GenAI Applications
  • Observability for Production LLM Systems
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