Demonstration of ModelDB: a system for managing machine learning models
ModelDB is an end to end system for managing machine learning models. It automatically tracks ML models and pipelines in their native environments, stores and indexes relevant data associated with pipelines, and allows users to run complex queries on this data. At the 2016 ICML ML Systems workshop, we presented our vision and early work on ModelDB. The system got a lot of interest from attendees as well as practicing data scientists. We have since expanded ModelDB capabilities and refined ModelDB to better support diverse workflows. In this paper, we propose to demonstrate ModelDB through two case studies from our early users. By means of these case studies, we seek to show how a model management system such as ModelDB can make the modeling process more efficient and make modeling data more accessible. We argue that by providing a system that tracks ML pipelines and metadata, supports model versioning, and allows easy comparison, we can give data scientists the power to quickly iterate on models, make results reproducible, and find insights faster.