Operationalize generative AI applications (GenAIOps)
At a glance
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To effectively scale generative Artificial Intelligence (AI) applications, you need to manage, deploy, and maintain GenAI apps to ensure their performance, reliability, and continuous improvement in real-world applications.
Prerequisites
Before starting this learning path, you should be familiar with fundamental generative AI concepts and services in Azure. Consider completing the Microsoft Azure AI Fundamentals: Generative AI learning path first.
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Modules in this learning path
Learn how to develop chat applications with language models using a code-first development approach. By developing generative AI apps code-first, you can create robust and reproducible flows that are integral for generative AI Operations, or GenAIOps.
Learn the fundamental concepts of evaluating generative AI applications.
Learn how to monitor the performance of your generative AI application using Microsoft Foundry. This module teaches you to track key metrics like latency and token usage to make informed, cost-effective deployment decisions.
Learn how to implement tracing in your generative AI applications using Microsoft Foundry and OpenTelemetry. This module teaches you to capture detailed execution flows, debug complex workflows, and understand application behavior for better reliability and optimization.