Track & Evaluate ML Model Experiments is an essential intermediate course for Machine Learning Engineers, Data Scientists, and MLOps practitioners aiming to elevate their process from ad-hoc scripting to a systematic, professional discipline. If you have ever faced the "it worked on my machine" problem or struggled to reproduce a great result from weeks ago, this course will provide you with the foundational MLOps practices to build a truly auditable and collaborative workflow. The primary goal is to empower you to manage the entire experiment lifecycle with confidence, ensuring that every model you build is reproducible, traceable, and ready for the rigors of production.

Throughout this course, you will get hands-on with industry-standard tools. You will learn to use Data Version Control (DVC) to version datasets and models with the same rigor you apply to code, creating a single source of truth for your team. You will then instrument training scripts with Weights & Biases (W&B) to automatically log every hyperparameter, metric, and artifact to a centralized, interactive dashboard. Finally, you will master a structured evaluation framework to make defensible model selections, moving beyond a single F1 score to balance predictive performance with critical operational constraints like latency and memory usage. Upon completion, you will have a complete toolkit for managing the ML lifecycle with clarity and precision. For learners interested in applying these MLOps skills to the next frontier, this course serves as a perfect foundation for more advanced topics, such as those covered in the LLM Engineering That Works: Prompting, Tuning & Retrieval course.