Data Science Version Control or DVC is an open-source tool for data science and
machine learning projects. With a simple and flexible Git-like architecture and
interface it helps data scientists:
* manage machine learning models - versioning, including data sets and
  transformations (scripts) that were used to generate models;
* make projects reproducible;
* make projects shareable;
* manage experiments with branching and metrics tracking.

It aims to replace tools like Excel and Docs that are being commonly used as a
knowledge repo and a ledger for the team, ad-hoc scripts to track and move
deploy different model versions, ad-hoc data file suffixes and prefixes.