ExperimentTracking
Experiment tracking refers to the practice of recording, organizing, and managing information about experiments in order to enable reproducibility, auditing, and collaboration. It is used across scientific research and data-driven fields such as machine learning, analytics, and software experiments. A typical experiment-tracking system stores data in hierarchies of experiments and runs. An experiment represents a collection of related runs often tied to a research question, project, or dataset. A run corresponds to a single execution of a process, capturing parameter settings, code version, environment details, timestamps, and user.
Key data tracked include hyperparameters or configuration settings, metrics (evaluations, loss, accuracy), artifacts (models, datasets, plots),
Common workflows involve defining the hypothesis or objective, configuring and executing experiments, logging results automatically or
Benefits include improved reproducibility, collaboration, governance, and traceability for research and product development. Challenges include interoperability