[8] Since then, a number of data version control systems, both open and closed source, have been developed and offered commercially,[9] with a subset dedicated specifically to machine learning.
[10] A wide range of scientific disciplines have adopted automated analysis of large quantities of data, including astrophysics, seismology, biology and medicine, social sciences and economics, and many other fields.
The principle of reproducibility is an important aspect of formalizing findings in scientific disciplines, and in the context of data science presents a number of challenges.
Some data version control tools allow users to create replicas of their production environment for testing purposes.
[12] It is possible that open source data version control software could eliminate the need for proprietary AI platforms by extending tools like Git and CI/CD for use by machine learning engineers.
[13] Many open-source solutions build on Git-like semantics to provide these capabilities, as Git itself was designed for small text files and doesn't support typical machine learning datasets, which are very large.
[14] Version control enables users to integrate with automation servers that allow establishing a CI/CD process for data.