Source: thenewstack.io
Why We Built an Open Source ML Model Registry with git
In speaking with many https://thenewstack.io/category/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.
ML teams implement a model registry solution to get centralized visibility and management of their models.
There are three major disconnects that I see when an ML team sets up a model registry: Disconnect between model and code lineage.
Using tools like GitHub Actions or GitLab CI, ML teams can easily push models into production through CI/CD within their model registry.
ML teams implement a model registry solution to get centralized visibility and management of their models.
There are three major disconnects that I see when an ML team sets up a model registry: Disconnect between model and code lineage.
Using tools like GitHub Actions or GitLab CI, ML teams can easily push models into production through CI/CD within their model registry.
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