Experiment tracking is a crucial aspect of machine learning and data science that involves systematically recording and managing the various experiments conducted during the model development process. As machine learning evolves, the complexity of experiments increases, making it essential to have a structured approach to track results, parameters, and configurations.

Traditionally, many practitioners relied on physical notebooks or spreadsheets to document their experiments. However, this method can be inefficient and prone to errors. Modern tools and frameworks, such as Weights & Biases (W&B) and Neptune, have emerged to facilitate experiment tracking, allowing teams to maintain a comprehensive record of their work. These tools enable users to visualize metrics, compare different runs, and trace issues back to specific experiments, which is vital for debugging and improving models ¹².

An effective experiment tracking workflow typically includes creating branches for experiments, running sub-experiments, and comparing results to identify the best-performing models. This structured approach not only enhances collaboration among team members but also simplifies the process of reproducing results ³⁴.