Practical MLOps: Build and Deploy ML Pipelines with MLflow and DVC

Transitioning a machine learning model from a local notebook to a reliable production environment is one of the biggest challenges in AI development today. This course bridges the gap between data science and software engineering by introducing you to the foundational principles of Machine Learning Operations (MLOps). Through structured, written explanations and practical code examples, you will learn how to build automated, reproducible, and monitored ML pipelines. You will progress from understanding core MLOps terminology to versioning datasets, tracking model experiments, and deploying production-ready APIs. What you'll learn: - Understand foundational MLOps concepts, lifecycle stages, and the core differences between DevOps and MLOps. - Track and register machine learning experiments using MLflow to ensure complete reproducibility. - Configure Data Version Control (DVC) to manage and version large datasets within your Git workflow. - Build and containerize machine learning microservices using FastAPI and Docker. - Apply basic CI/CD principles and low-code AutoML tools to automate model training and evaluation. - Implement model monitoring and basic observability practices to detect data drift in production. The course begins with essential terminology and the MLOps lifecycle before guiding you step-by-step through data versioning, experiment tracking, and containerized deployment. This course is designed for beginners, aspiring data scientists, and software engineers looking to enter the field of MLOps, with no prior operations experience required. Start reading today to build reliable, production-ready machine learning pipelines.