Track and Evaluate ML Model Experiments

Overview

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.

Syllabus

Data Versioning and Artifact Management

This module tackles the foundational challenge of managing datasets and models. Learners will discover why ad-hoc file naming fails at scale and will learn to use Data Version Control (DVC) to create a single source of truth. They will get hands-on experience initializing DVC in a Git repository, tracking data artifacts, and configuring remote storage to ensure experiments are fully reproducible.

Experiment Tracking and Management

With data versioning in place, this module focuses on tracking the experiments themselves. Learners will move beyond messy spreadsheets and learn to use Weights & Biases (W&B) to systematically log hyperparameters, metrics, and artifacts. They will instrument a real ML training script to create a rich, interactive, and collaborative record of their experimentation process.

Model Evaluation and Selection

This final module focuses on the crucial decision-making process. Learners will use the data they have tracked to make an informed, evidence-based choice about which model is best for production. They will learn to balance predictive performance with operational constraints and to document their decision in a way that ensures auditability and stakeholder trust.