Data science teams struggle with managing multiple experiments and models and need an efficient way to store, retrieve, and utilize details like model versions, hyperparameters, and performance metrics. In this article, you will learn about the challenges plaguing the ML space - and why conventional tools are not the right answer to them.
Table of contents
ML model versioning: where are we at?Is Git the solution for versioning ML models?Why should we look beyond Git?Traits of an ideal tool for Machine Learning experimentationAlternatives to Git for ML model versioningHow to choose the right tool for versioning ML models?Wrapping up!Sort: