How the Integrations Between Ray & MLflow Aids Distributed ML Production

In this blog post, we're announcing two new integrations with Ray and MLflow: Ray Tune MLflow Tracking and Ray Serve MLflow Models, which together make it much easier to build machine learning (ML) models and take them to production. These integrations are available in the latest Ray wheels. You can follow the instructions here to pip install the nightly version of Ray and take a look at the documentation to get started. They will also be in the next Ray release -- version 1.2 Our goal is to leverage the strengths of the two projects: Ray's distributed libraries for scaling training and serving and MLflow's end-to-end model lifecycle management. Let's first take a brief look at what these libraries can do before diving into the new integrations.