Governance, Cost Management & Production Excellence

Microsoft via Coursera

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Overview

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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.

Syllabus

FinOps: Token Economics and Cost Modeling
- This module teaches you to understand and model the token-based cost structure of generative AI solutions, enabling accurate cost forecasting and informed optimization decisions.
FinOps: Cost Governance and Optimization
- This module teaches you to design and implement FinOps governance frameworks that provide cost visibility, enforce budgets, and enable chargeback across enterprise GenAI platforms.
AI Governance: Unified Access Layer and Policy Enforcement
- This section teaches you to design governance frameworks that provide centralized control over AI model access while enabling appropriate use across the enterprise.
AI Governance: Audit Logging and Compliance
- This section teaches you to design audit logging and traceability controls that meet compliance requirements and enable effective incident investigation.
Content Safety: Configuration and Protection
- This module teaches you to configure comprehensive content safety controls that protect users and organizations from harmful AI outputs and malicious inputs.
Content Safety: Incident Response
- This module teaches you to design incident-response processes that detect, classify, and respond to content-safety violations effectively.
Feedback Loops: Collection and Annotation
- This section teaches you to design comprehensive feedback collection systems that capture user signals, corrections, and expert annotations for AI improvement.
Feedback Loops: Continuous Improvement Process
- This module teaches you to design closed-loop processes that systematically convert feedback into improvements across prompts, data, and models.
Observability and Version Management
- This module teaches you to design observability and version management frameworks for multi-agent systems that provide visibility into autonomous agent behavior.
Failure Modes and Mitigation
- This module teaches you to analyze common agent failure modes and design detection and mitigation strategies that prevent or contain failures.
Decision Attribution and Accountability
- This module teaches you to design decision attribution frameworks that enable tracing agent decisions to their inputs and establishing human accountability for high-impact agent actions.
End-to-End Design: Operating Model Components
- This section teaches you to identify all components of a complete GenAIOps operating model and understand how they integrate into a coherent system.
End-to-End Design: Complete Operating Model Documentation
- This section teaches you to produce comprehensive operating model documentation that serves as the operational foundation for enterprise AI platforms.
Maturity Assessment: Applying the Maturity Model
- This section teaches you to apply the Microsoft GenAIOps Maturity Model to assess organizational capabilities and identify specific gaps preventing advancement to higher maturity levels.
Maturity Assessment: Improvement Planning
- This module teaches you to translate maturity assessments into actionable improvement plans with prioritized initiatives and realistic timelines.
Project Module: Complete GenAIOps Operating Model
- In this capstone project, learners design a complete GenAIOps operating model for a complex enterprise scenario. They'll integrate all governance, cost management, safety, and operational excellence practices into a comprehensive operating model document, conduct a maturity assessment, and create a prioritized improvement plan, demonstrating mastery of enterprise GenAIOps.