Data Version Control or DVC is an open-source tool for data science and machine learning projects.
- simple command line Git-like experience. Does not require installing and maintaining any databases. Does not depend on any proprietary online services;
- it manages and versions datasets and machine learning models. Data is saved in S3, Google cloud, Azure, Alibaba cloud, SSH server, HDFS or even local HDD RAID;
- it makes projects reproducible and shareable, it helps answering question how the model was build;
- it helps manage experiments with Git tags or branches and metrics tracking; DVC aims to replace tools like Excel and Google 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.