Azure AI Fundamentals

This unit introduces core concepts of artificial intelligence, Microsoft Azure AI workloads, and foundational machine learning principles, including model lifecycle and common cloud architectures. It also covers Responsible AI principles and Azure tools for fairness, privacy, and reliability to prepare students for deeper hands-on deployment and model development in subsequent units.

Learning Objectives

• Analyze core AI concepts and distinguish types of AI workloads, client-server and cloud application architectures, and common Azure AI services
• Apply cloud computing concepts to plan and map AI workloads in Azure, including content moderation, personalization, knowledge mining, and document intelligence
• Evaluate Responsible AI principles (fairness, reliability and safety, privacy and security, inclusiveness, transparency and accountability) and select appropriate Azure tools and practices to mitigate risks
• Demonstrate foundational ML and model lifecycle skills by describing data requirements, training/validation/testing processes, pretrained vs custom models, deployment basics, continuous learning, and identifying overfitting and underfitting