Version control is used to keep track of modifications made in a
software code. Similarly, when building machine learning (ML)
systems, it is essential to track things, such as the datasets used
to train the model, the hyperparameters and pipeline used, the
version of tensorflow used to create the model, and many more.
ML artifacts’ history and lineage are very complicated than a
simple, linear log. Git can be used to track the code to one
extent, but we need something to track your models, datasets, and
more. The complexity of ML code and artifacts like models,
datasets, and much more requires a similar approach.