The Generative AI Application Lifecycle [Pt 14] | Generative AI for Beginners

Post Content

​ Find the full “Generative AI for Beginners” Course here ➡️ https://aka.ms/genai-beginners

An important question for all AI applications is the relevance of AI features, as AI is a fast evolving field, to ensure that your application remains relevant, reliable, and robust, you need to monitor, evaluate, and improve it continuously. This is where the generative AI lifecycle comes in.

The generative AI lifecycle is a framework that guides you through the stages of developing, deploying, and maintaining a generative AI application. It helps you to define your goals, measure your performance, identify your challenges, and implement your solutions. It also helps you to align your application with the ethical and legal standards of your domain and your stakeholders. By following the generative AI lifecycle, you can ensure that your application is always delivering value and satisfying your users.

? In this video, we cover:
✅ Understand the Paradigm Shift from MLOps to LLMOps
✅ The LLM Lifecycle
✅ Lifecycle Tooling
✅ Lifecycle Metrification and Evaluation

Tutorial: Build and deploy a question and answer copilot with prompt flow in Azure AI Studio: https://learn.microsoft.com/en-us/azure/ai-studio/tutorials/deploy-copilot-ai-studio?WT.mc_id=academic-105485-koreyst
Contoso Chat sample: https://github.com/Azure-Samples/contoso-chat/

? After completing this lesson, check out our Generative AI Learning collection – https://aka.ms/genai-collection to continue leveling up your Generative AI knowledge!   Read More Microsoft Developer