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AI Governance & Operations in .NET

Description

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.

 

🕒 Duration: 8 hours

👥 Target Audience:

  • Roles:
    Software Engineers/ Architects (.NET, Backend, Full stack)
    DevOps / SRE Engineers
    CTOs / Engineering Managers 
  • Seniority:
    Mid-Senior
Webinar Content
Module 1: 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
Module 2: 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
Module 3: 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
Module 4: 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
Learning Objectives:

After attending this webinar 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
Prerequisite Knowledge
  • Experience with ASP.NET Core Web API and basic Azure services
  • Familiarity with AI concepts (LLMs, APIs, or RAG fundamentals)
Tags:

Date

28 May 2026, 16:00-19:00

Cost

Free

Labels

AI-Native
Category

Organizer

Code.Hub
Email
[email protected]

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