I have been working with MLflow tools for a few months that's why I decided to show to Data Scientists and ML developer how to leverage MLflow as a platform to track experiments, package projects to reproduce runs, use model flavors to deploy in diverse environments, and manage models in a central respository for sharing. For quick start you can easily clone my github repository to use all notebooks in your own workspace. You should create a workspace using AWS Account. Once it's ready, import first file to your workspace to see full code and try in on your own. Then you need to create a cluster.

We are excited to announce new enterprise grade features for the MLflow Model Registry on Databricks. The Model Registry is now enabled by default for all customers using Databricks' Unified Analytics Platform. In this blog, we want to highlight the benefits of the Model Registry as a centralized hub for model management, how data teams across organizations can share and control access to their models, and touch upon how you can use Model Registry APIs for integration or inspection. 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. Built on these existing capabilities, the MLflow Model Registry [AWS] [Azure] provides a central repository to manage the model deployment lifecycle.

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.

AMSTERDAM and SAN FRANCISCO, Oct. 16, 2019 – Databricks, the leader in unified data analytics, today 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 phases at any given time can span into the thousands," said Matei Zaharia, co-founder and CTO at Databricks.