Why We Built an Open Source ML Model Registry with git

In speaking with many machine learning teams, we've found that implementing a model registry has become a priority for AI-first organizations in solving visibility and governance concerns. A model registry is a centralized model store to collaboratively manage the full lifecycle of ML models. This includes model lineage and versioning, moving models between stages from development to staging to production, and model annotations and discovery (i.e., timestamps, descriptions, labels, etc.). ML teams implement a model registry solution to get centralized visibility and management of their models. But there are challenges to adopting a model registry, making it hard to build an up-to-date model registry that contains everything an organization needs.