Much of the work performed by Data Science teams remains buried far away from business users. To generate real value from investment in machine learning, companies must bring these resources into the light of day. This presentation will be a case study on how a wide array of data science assets can be published and governed in a centralized platform.

This will cover:
– Consolidating a heterogeneous set of data science assets under a single pane of glass
– Capturing & sharing metadata to promote discoverability
– Enabling enterprise-wide search for maximum adoption and reusability