What you'll learn

Production AI workloads fail differently than deterministic services: quality drifts silently, costs spike without a single error, and guardrails gap while every availability dashboard stays green.
In this course, Operational Considerations for AI and ML Workloads for Azure, you’ll gain the ability to architect and defend a production-operations solution for AI workloads on Azure. First, you’ll explore how to derive the operational rigor a workload must guarantee - quality, drift, cost, latency, and auditability - from its stated risk profile. Next, you’ll discover how to resolve each rigor requirement into Azure's monitoring and evaluation capabilities, including Microsoft Foundry Observability tracing and continuous evaluation, integrated into your existing Azure Monitor, Application Insights, and Log Analytics stack. Finally, you’ll learn how to enforce guardrails with Azure AI Content Safety and Microsoft Defender for Cloud, assess whether your architecture actually surfaces degradation under real conditions, and reason through incident-response and rollback paths for non-deterministic failure across model versions, prompts, and retrieval indexes.
When you’re finished with this course, you’ll have the skills and knowledge of AI operations on Azure needed to architect and defend a production-operations solution for AI workloads.