Microsoft Generative AI Operations (GenAIOps)

Microsoft via Coursera Professional Certificate

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Generative AI is transforming enterprises, but moving from prototype to production requires a new operational discipline. This Professional Certificate teaches Microsoft's GenAIOps framework: the practices, tools, and architectures needed to deploy, monitor, and govern generative AI at enterprise scale on Azure. You'll design end-to-end GenAIOps operating models integrating DataOps for grounding data, PromptOps for versioning and experimentation, RAG pipeline optimization, and automated evaluation frameworks. You'll build CI/CD pipelines with progressive deployment, production observability, cost governance, content safety configurations, and continuous improvement loops. Hands-on activities use Microsoft Foundry, Azure OpenAI Service, and Azure API Management to configure real enterprise environments. Each course includes design artifacts—architecture diagrams, runbooks, policies, and cost models—building a portfolio that demonstrates your ability to design production-grade GenAI systems. You'll be prepared to implement and operate production GenAIOps systems that are reliable, safe, cost-effective, and continuously improving. Who this is for: ML engineers, MLOps practitioners, platform engineers, and cloud engineers with foundational cloud/ML experience who want to specialize in operationalizing generative AI on Azure. Activities require an Azure subscription with Microsoft Foundry access; enterprise access is expected.

Syllabus

Course 1: GenAIOps Foundations & Data Operations
Course 2: Prompt, RAG & Evaluation Operations
Course 3: CI/CD, Deployment & Observability
Course 4: Governance, Cost Management & Production Excellence
Course 5: Launching Your GenAIOps Career

Courses

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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.
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Production generative AI systems require more than good prompts; they need systematic experimentation, rigorous evaluation, and managed model customization. This course teaches the core operational practices that make GenAI solutions reliable and continuously improvable. You'll design prompt versioning and variant experimentation frameworks, including A/B testing methodology, hyperparameter tracking, and rollback procedures using Microsoft Foundry. You'll run structured RAG optimization experiments across chunking strategies, embedding model variants, and retrieval configurations, evaluating each using groundedness, relevance, coherence, and fluency metrics via the Azure OpenAI Evaluation SDK. From there, you'll build automated evaluation frameworks with quality and safety thresholds, human-in-the-loop review triggers, and integration with CI/CD release decisions. You'll also design the fine-tuning operations lifecycle, including dataset versioning, LoRA configuration, model registry management, and evaluation against baseline models.
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Getting generative AI into production and keeping it reliable requires engineering-grade deployment and monitoring systems. This course teaches you to build the infrastructure enterprise GenAI solutions depend on. You'll design CI/CD pipelines using GitHub Actions or Azure DevOps with automated evaluation gates, environment promotion, and rollback triggers for prompt, model, and data updates. You'll implement rollout strategies, including blue/green deployments, A/B traffic splits, and canary releases using Azure API Management with per-team quotas and token-based rate limiting. The course covers orchestrator deployment using managed online endpoints, Azure Container Apps, and Foundry Agent Service. You'll also build observability architectures with Foundry Observability, Azure Monitor, and Application Insights for distributed tracing, latency dashboards, and token tracking. Finally, you'll learn LLM monitoring techniques to detect hallucinations, groundedness degradation, and semantic drift, and investigate prompt drift, data staleness, and model regression.
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Running generative AI at enterprise scale requires more than deployment. It demands governance, financial discipline, safety controls, and continuous improvement. This course teaches the operational practices that keep production GenAI platforms reliable, compliant, and cost effective. You'll design FinOps frameworks for token-based cost management, including forecasting, per-team chargeback with Azure API Management, and optimization through model routing, caching, and provisioned throughput. You'll implement governance using Azure Policy and Azure API Management with audit logging for compliance and incident investigations. The course also covers content safety using Azure OpenAI Content Safety, Prompt Shield for injection defense, and incident response with severity classification and rollback triggers. You'll design feedback-driven improvement systems, multi-agent operational frameworks with decision attribution and circuit breaker patterns, and end-to-end GenAIOps operating models. The course concludes with a GenAIOps maturity assessment using the Microsoft GenAIOps Maturity Model to identify strengths and plan future improvements.
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Completing a certificate is just the first step—landing the right role requires positioning your new skills effectively. This course prepares you to pursue GenAIOps roles by helping you articulate your skills, build a compelling portfolio, and prepare for technical interviews. You'll explore role profiles for GenAIOps Engineer, ML Platform Engineer, AI Operations Specialist, and AI Solutions Architect—learning which program skills are most relevant for each and how to position yourself effectively. You'll build a portfolio showcasing your program projects, optimize your LinkedIn profile, and organize your work on GitHub to demonstrate production AI expertise. You'll also prepare for technical interviews covering common formats: technical screening, system design, behavioral scenarios, and case studies. You'll practice discussing evaluation frameworks, CI/CD for AI, observability, cost management, and governance. By the end of this course, you'll have a compelling career narrative and the confidence to pursue GenAIOps opportunities.