Model Management and Responsible AI in Azure Machine Learning

Deploying a machine learning model is only the first step; ensuring it remains reliable, fair, and easy to track is where true MLOps begins. This course teaches you how to govern your models effectively using Azure Machine Learning. You will transition from training isolated models to managing a structured machine learning lifecycle. Through clear written explanations and practical configuration examples, you will learn to register models, track performance metrics, and evaluate models for fairness and bias. What you'll learn: - Understand foundational model management and MLOps concepts within the Azure ecosystem - Track model parameters, metrics, and artifacts using MLflow integration - Register, version, and organize models in the Azure Machine Learning registry - Configure and interpret Responsible AI dashboards to evaluate model fairness and explainability - Analyze model predictions and identify potential biases before production deployment The course begins with core model registry concepts and MLflow integration, then moves into practical tracking workflows, and concludes with setting up comprehensive Responsible AI dashboards to assess model performance and ethics. This course is designed for beginner data scientists, aspiring MLOps engineers, and cloud enthusiasts who want to manage models responsibly. No prior experience with Azure Machine Learning is required, though a basic understanding of machine learning concepts is helpful. Start reading today to master professional model governance and responsible AI practices in the cloud.