Introducing the MLflow Model Registry--Machine Learning Model Hub

At today's Spark AI Summit in Amsterdam, we announced the availability of the MLflow Model Registry, a new component in the MLflow open source ML platform. Since we introduced MLflow at Spark AI Summit 2018, the project has gained more than 140 contributors and 800,000 monthly downloads on PyPI, making MLflow one of the fastest growing open source projects in machine learning! MLflow already has the ability to track metrics, parameters, and artifacts as part of experiments, package models and reproducible ML projects, and deploy models to batch or real-time serving platforms. The MLflow Model Registry builds on MLflow's existing capabilities to provide organizations with one central place to share ML models, collaborate on moving them from experimentation to testing and production, and implement approval and governance workflows. Since we started MLflow, model management was the top requested feature among our open source users, so we are excited to launch a model management system that integrates directly with MLflow.