Overview

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Generative AI in production requires more than a working model; it demands operational discipline. This course establishes the conceptual and technical foundation for GenAIOps, helping you understand how it differs from traditional MLOps and how to apply it on Azure. You'll learn to map the complete GenAIOps operating loop from Define & Explore through Build, Evaluate, Deploy, Monitor, and Feedback to specific Azure tools and team responsibilities. You'll evaluate implementation starting points, including Azure OpenAI SDK-based templates and platform-native toolchain options, and choose the right fit for different solution types. The course then shifts to DataOps: the data management discipline that powers reliable RAG solutions. You'll design grounding data ingestion pipelines, chunking and embedding versioning strategies, vector store index maintenance workflows, and data freshness SLAs. You'll also learn to implement right-to-be-forgotten controls, data lineage tracking, and grounding content validity audit processes for compliance-sensitive environments.

Syllabus

MLOps to GenAIOps: Key Differences
- Examine the five key dimensions where GenAIOps differs from MLOps: data management and data quality, model behavior, evaluation, governance, and cost. You will map your existing MLOps investments to their GenAIOps equivalents and identify which components transfer directly, which require adaptation, and which must be built from scratch.
GenAIOps Maturity: Assess and Plan
- Microsoft GenAIOps Maturity Model gives you the tools to assess an organization's GenAIOps readiness. You will learn to score capability maturity across four dimensions, identify the most impactful gaps, and produce a prioritized improvement plan with concrete actions achievable in 30–60 days.
Operating Loop: Inner and Outer Loops on Azure
- This section introduces the six phases of the GenAIOps operating loop and maps each phase to specific Azure services and capabilities, providing you with a clear mental model for organizing your GenAIOps practice.
Operating Loop: Evaluating GenAIOps Implementation Starting Points
- This section teaches you to evaluate Microsoft's GenAIOps accelerator templates, understand their architectural differences, and select the right starting point for your solution type.
DataOps: Grounding Data Pipelines and Versioning
- This module teaches you to design robust grounding data pipelines that handle multiple source types, implement versioning strategies for chunking and embeddings, and maintain vector store indexes for production RAG solutions.
DataOps: Data Freshness and Compliance Controls
- This module teaches you to define and enforce data freshness SLAs for grounding data and implement compliance controls including right-to-be-forgotten handling and data lineage tracking.
DataOps: Grounding Content Validity
- This module teaches you to design grounding content validity audit processes that detect when grounding data has become outdated due to domain changes, ensuring your RAG solutions don't serve information that was once correct but is no longer valid.
Project Module: GenAIOps Foundation Design
- In this project, you'll synthesize your skills by producing a foundation-level GenAIOps design for a realistic enterprise scenario. You'll assess organizational maturity, map the operating loop to Azure tools, and design a grounding data architecture for a claims knowledge assistant, producing portfolio-ready documentation.