Description
Existing production machine learning systems often suffer from various problems that make them hard to use. For example, data scientists and ML practitioners often spend most of their time stitching and managing bespoke distributed systems to build end-to-end ML applications and push models to production.
To address this, the Ray community has built Ray AI Runtime (Ray AIR), an open-source toolkit for building large-scale end-to-end ML applications.