AI-300: Azure Machine Learning Operations Engineer Exam Prep

<div>The AI-300: Microsoft Machine Learning Operations Engineer Associate certification focuses on one of the most important skills in modern AI — MLOps. While many AI courses focus only on building models, real-world machine learning systems require reliable deployment, monitoring, governance, and optimization. That is exactly what this certification — and this course — focuses on.</div><div><br></div><div>In this course, you will learn how to design, deploy, and manage machine learning systems using Azure Machine Learning and Microsoft Foundry. We will cover both classical machine learning workflows as well as modern GenAI and agent-based systems, giving you a comprehensive understanding of the AI-300 exam topics.</div><div><br></div><div>You will start by learning the fundamentals of machine learning operations, including model training workflows, MLflow tracking, and Azure ML pipelines. From there, we will explore how to deploy models to production endpoints and monitor them effectively.</div><div><br></div><div>The course then moves into modern GenAI architectures, including Prompt Flow microservices, Retrieval-Augmented Generation (RAG) systems, and AI agents using Microsoft Foundry. You will also learn how to fine-tune and optimize large language models and build scalable GenAI systems.</div><div><br></div><div>Finally, we will cover responsible AI practices, monitoring strategies, model performance optimization, and data drift detection to ensure your AI systems remain reliable and safe in production.</div><div><br></div><div>By the end of this course, you will have a strong understanding of MLOps on Azure and be well prepared to take the Microsoft AI-300 certification exam.</div><div><br></div><div>If you're looking to deploy real-world AI systems and master MLOps with Azure, this course is for you.</div>

What you'll learn:

• Understand MLOps concepts and workflows using Azure Machine Learning
• Build and orchestrate ML training pipelines using Azure ML and MLflow
• Deploy, manage, and monitor machine learning models in production
• Design GenAI systems using Prompt Flow, RAG architectures, and Microsoft Foundry