In today's data-driven world, machine learning experts and data scientists deal with a large volume of datasets, files, and metrics to carry out day-to-day operations. The varying versions of these artifacts need to be tracked and managed as experiments are performed on them in multiple iterations. Data Version Control is a great practice for managing numerous datasets, machine learning models, and files in addition to keeping a record of multiple iterations – i.e. when, why, and what was altered.
This is a companion discussion topic for the original entry at https://blog.crowdbotics.com/data-version-control-explained/