MLOps Foundations: Managing ML Lifecycles with MLflow and Hugging Face

Moving machine learning models from local notebooks to reliable production systems requires robust operations. This text-based course guides you through the foundational tools of modern MLOps, focusing on MLflow and Hugging Face. You will transition from writing isolated machine learning code to managing a structured, reproducible ML lifecycle. By understanding how to track experiments, package models, and leverage pre-trained assets, you will build the skills needed to make your AI projects reliable and maintainable. What you'll learn: - Understand core MLOps principles, starting with foundational terminology and lifecycle stages. - Track machine learning experiments, parameters, and performance metrics using MLflow Tracking. - Manage and version your models systematically using the MLflow Model Registry. - Explore the Hugging Face ecosystem to find, load, and fine-tune pre-trained models. - Apply reproducible workflows to ensure your machine learning projects are ready for deployment. - Track modern workloads, including basic experiment logging for large language models. The course begins with essential MLOps concepts and vocabulary before moving into step-by-step written guides and configuration examples. You will progress from basic experiment tracking to managing production-ready model registries through clear explanations and structured code snippets. This course is designed for aspiring ML engineers, data scientists, and software developers who are new to MLOps, with no prior DevOps or advanced infrastructure experience required. Start reading today to build a reliable foundation for your machine learning operations.