AI Governance & Operations in .NET
Certificate of Completion by Code.Hub
This course focuses on establishing robust governance, observability, and operational excellence for AI-enabled .NET systems in enterprise environments. It covers responsible AI principles, policy enforcement, monitoring pipelines, cost control, and lifecycle management of AI components such as LLMs, RAG systems, and agents. Participants will learn how to operationalize AI in production using .NET, Azure, and modern DevOps/MLOps practices, ensuring compliance, reliability, and performance. The course bridges the gap between AI engineering and enterprise governance, enabling scalable and auditable AI solutions.
By the end of this module, participants will be able to:
- Design governance frameworks for AI-enabled .NET applications
- Implement observability and monitoring for LLM-based systems
- Apply responsible AI principles (fairness, security, compliance)
- Manage AI lifecycle (versioning, evaluation, deployment) in production
- Optimize cost, performance, and reliability of AI services
AI Governance Foundations Responsible AI Principles Risk & Compliance in AI Systems
Introduction to AI Governance
- Governance pillars: accountability, transparency, auditability
- Responsible AI (bias, fairness, explainability)
- Regulatory context (GDPR, EU AI Act overview)
- AI risk identification in .NET systems
AI Lifecycle Management Model & Prompt Versioning Evaluation Pipelines
Managing AI in Production
- – Versioning models, prompts, embeddings
- Evaluation strategies (offline/online, human-in-the-loop)
- CI/CD integration for AI components
- A/B testing for LLM outputs
Observability & Monitoring Logging & Telemetry Incident Handling
AI System Observability
- – Logging prompts/responses safely (PII considerations)
- Metrics: latency, token usage, hallucination rate proxies
- Azure Monitor, App Insights, OpenTelemetry in .NET
- Alerting and incident response workflows
Cost Optimization & Security Access Control & Secrets Operational Excellence
Secure & Efficient AI Operations
- Cost control (token usage, caching, batching)
- Secure API usage (Azure Key Vault, Managed Identity)
- Role-based access for AI features
- Operational runbooks and SLAs for AI services
Roles:
- Software Engineers/ Architects (.NET, Backend, Full stack)
- DevOps / SRE Engineers
- CTOs / Engineering Managers
Seniority:
- Mid-Senior
- Experience with ASP.NET Core Web API and basic Azure services
- Familiarity with AI concepts (LLMs, APIs, or RAG fundamentals)
Sessions can be delivered via the following formats:
- Live Online – Interactive virtual sessions via video conferencing
- On-Site – At your organization’s premises
- In-Person – At Code.Hub’s training center
- Hybrid – A combination of online and in-person sessions
Interested for
