MLOps Foundations with MLflow and Hugging Face

As machine learning moves from research to production, the ability to track, manage, and deploy models efficiently is now a critical skill. This course provides a clear path for beginners to understand the operational side of machine learning using two of the most influential tools in the field. You will transition from running isolated scripts to managing professional machine learning lifecycles with confidence. Through structured text-based lessons, you will learn how to maintain reproducibility, version your models, and collaborate effectively using modern MLOps patterns. * Understand the core principles of MLOps and the importance of experiment tracking * Configure MLflow to log parameters, metrics, and artifacts for reproducible results * Manage the model lifecycle using a centralized model registry for version control * Explore the Hugging Face Hub to discover, use, and share pre-trained models and datasets * Apply best practices for organizing machine learning projects to ensure scalability * Integrate model tracking into existing Python workflows for better observability The course begins with essential terminology and the foundational concepts behind MLOps before moving into the practical implementation of tracking and repository management. You will progress from basic experiment logging to model versioning and repository interactions through detailed written explanations and code examples. This course is designed for beginners in data science and machine learning who want to learn the operational side of the field; no prior experience with MLOps tools is required. Start building a more professional and reproducible machine learning workflow today.