ML Experiment Tracking and Model Development for MLOps

Moving from messy experimental code to organized, production-ready machine learning workflows can be challenging without a systematic approach. This course teaches you how to log, track, and compare your ML experiments to ensure complete reproducibility. You will transition from chaotic development cycles to structured model management, learning how to log parameters, compare model runs, and manage model versions using industry-standard MLOps practices. What you'll learn: - Understand the core principles of experiment tracking and why it is essential for modern MLOps - Log hyperparameters, metrics, and artifacts systematically during model training - Compare different model runs to identify the best-performing configurations - Manage model versions and transitions throughout the development lifecycle - Apply clean code practices and modern packaging standards to your ML workflows - Organize your machine learning projects for seamless collaboration and reproducibility The course begins with foundational definitions and the core concepts of experiment tracking before guiding you through step-by-step written explanations of logging, comparing runs, and versioning models. You will practice these concepts through text-based exercises designed to simulate real-world MLOps scenarios. This course is designed for beginner data scientists, machine learning enthusiasts, and software engineers looking to adopt MLOps best practices. No advanced machine learning background or prior DevOps experience is required. Start structured tracking today to make your machine learning workflows organized and reproducible.