As data scientists iteratively develop models, they often experiment with datasets, features, libraries, algorithms, and parameters.
Even small changes can significantly impact the resulting model. This means data scientists need the ability to iterate and repeat similar experiments in parallel and on demand, as they rely on differences in output and scores to tune parameters until they obtain the best fit for the problem at hand. Such a training workflow requires versioning of the file system, input parameters, and output of each training run.
Without versioned experiments you would need intense process rigor to consistently track training artifacts (data, parameters, code, etc.), and even then it might be impossible to reproduce and explain a given result. This can lead to wasted time/effort during collaboration, not to mention the compliance risks introduced.
Starting with version 1.4, Cloudera Data Science Workbench uses experiments to facilitate ad-hoc batch execution and model training. Experiments are batch executed workloads where the code, input parameters, and output artifacts are versioned. This feature also provides a lightweight ability to track output data, including files, metrics, and metadata for comparison.