AI 102 Microsoft Azure AI Engineer Associate Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview: This course is designed for intermediate learners aiming to become certified Microsoft Azure AI Engineers. It provides a comprehensive, hands-on curriculum focused on building, deploying, and managing AI solutions using Azure AI services. The program spans approximately 15-18 hours of learning across six modules, combining foundational AI concepts with practical implementation. Learners will engage in guided projects, interactive labs, and real-world case studies to gain career-ready skills in AI engineering on the Azure platform.
Module 1: Foundations of Computing & Algorithms
Estimated time: 4 hours
- Introduction to key concepts in foundations of computing & algorithms
- Applying computational thinking to solve complex engineering problems
- Designing algorithms that scale efficiently with increasing data
- Interactive lab: Building practical solutions
- Guided project work with instructor feedback
Module 2: Neural Networks & Deep Learning
Estimated time: 3 hours
- Understanding core AI concepts including neural networks and deep learning
- Hands-on exercises applying neural networks & deep learning techniques
- Implementing intelligent systems using modern frameworks and libraries
- Interactive lab: Building practical solutions
- Quiz and peer-reviewed assignment
Module 3: AI System Design & Architecture
Estimated time: 4 hours
- Introduction to key concepts in AI system design & architecture
- Hands-on exercises applying AI system design & architecture techniques
- Case study analysis with real-world examples
- Evaluating model performance using appropriate metrics and benchmarks
- Assessment: Quiz and peer-reviewed assignment
Module 4: Natural Language Processing
Estimated time: 2 hours
- Hands-on exercises applying natural language processing techniques
- Implementing prompt engineering techniques for large language models
- Discussion of best practices and industry standards
- Assessment: Quiz and peer-reviewed assignment
- Guided project work with instructor feedback
Module 5: Computer Vision & Pattern Recognition
Estimated time: 3 hours
- Hands-on exercises applying computer vision & pattern recognition techniques
- Review of tools and frameworks commonly used in practice
- Case study analysis with real-world examples
- Guided project work with instructor feedback
Module 6: Deployment & Production Systems
Estimated time: 2 hours
- Introduction to key concepts in deployment & production systems
- Review of tools and frameworks commonly used in practice
- Discussion of best practices and industry standards
- Assessment: Quiz and peer-reviewed assignment
Prerequisites
- Prior knowledge of Azure fundamentals
- Programming fundamentals (Python or similar)
- Familiarity with basic machine learning concepts
What You'll Be Able to Do After
- Design and implement scalable AI algorithms on Azure
- Build and deploy intelligent systems using Azure AI services
- Apply deep learning and NLP techniques to real-world problems
- Deploy AI models into production environments with best practices
- Earn the Microsoft Azure AI Engineer Associate (AI-102) certification readiness