Elective

AI Engineering (MLOps Foundations)

From GenAIOps fundamentals to production deployment — build, evaluate, and operate AI systems on Azure.

Duration

12 weeks, 24 sessions

Audience

Engineers and advanced practitioners who need end-to-end ML delivery on Azure.

Prerequisites

Python, Git, and basic cloud concepts recommended.

Tools

Python Azure AI SDKs Azure AI Search Prompt Flow Docker GitHub Actions Bicep / IaC

Learning Outcomes

✓ Set up secure development environments and infrastructure as code with Bicep
✓ Build and evaluate RAG systems with vector search, semantic ranking, and hybrid retrieval
✓ Design agentic AI workflows and orchestrate multi-step pipelines
✓ Deploy models via containers and serverless endpoints with CI/CD automation
✓ Implement observability, tracing, and operational dashboards for production AI

Week	Session A	Session B
1	Evolution to GenAIOps	Secure Development Environment
2	Infrastructure as Code (IaC) with Bicep	Data Versioning Strategy
3	Azure AI SDKs & Python	Capstone Design & Architecture
4	Azure OpenAI Service Deep Dive	Systematic Prompt Engineering
5	Introduction to Prompt Flow	Custom Logic with Python Tools
6	Variants & Automated Evaluation	Mid-Term Review & Git Strategy
7	Vector Search Fundamentals	Data Ingestion & Chunking
8	Hybrid Search & Semantic Ranker	Building RAG Flows
9	Evaluating RAG Systems	Agentic Workflows
10	Containerization	Serverless Deployment
11	Managed Endpoints & Traffic Splitting	CI/CD for GenAI
12	Observability & Tracing	Operational Dashboards

Attendance 40%

Expert Lecture Participation 30%

Exams (Midterm + Final) 30%

Pass: Grade D or above (≥60%) with ≥75% attendance

Grades: A (90–100) · B (80–89) · C (70–79) · D (60–69) · F (<60)

Python Azure AI SDKs Azure AI Search Prompt Flow Docker GitHub Actions Bicep / IaC

Join the next cohort and start building real-world AI skills.

Applications are open. Secure your place in the next cohort and start your AI journey.