Databricks, the leader in unified data analytics, announced Model Registry, a new capability within MLflow, an open-source platform for the machine learning (ML) lifecycle created by Databricks.
The new component enables a comprehensive model management process by providing data scientists and engineers a central repository to track, share, and collaborate on machine learning models. The Model Registry manages the full lifecycle of models and their stage transitions from experimentation to staging and deployment.
Since introducing MLflow at Spark+AI Summit 2018, the project has more than 140 contributors and 800,000 monthly downloads making it the leader in ML lifecycle management.
“Everyone who has tried to do machine learning development knows that it is complex. The ability to manage, version and share models is critical to minimizing confusion as the number of models in experimentation, testing and production ph ..
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