MARVIN: An Open Machine Learning Corpus and Environment for Automated Machine Learning Primitive Annotation and Execution
This tool addresses the problem of automating ML pipeline construction for researchers and practitioners, but it is incremental as it builds on existing libraries and frameworks.
The paper introduces MARVIN, a tool for automated machine learning that provides an environment to locate, annotate, and execute ML primitives from libraries like Scikit-Learn and Keras, enabling composition of ML pipelines with over 400 datasets and challenge problems.
In this demo paper, we introduce the DARPA D3M program for automatic machine learning (ML) and JPL's MARVIN tool that provides an environment to locate, annotate, and execute machine learning primitives for use in ML pipelines. MARVIN is a web-based application and associated back-end interface written in Python that enables composition of ML pipelines from hundreds of primitives from the world of Scikit-Learn, Keras, DL4J and other widely used libraries. MARVIN allows for the creation of Docker containers that run on Kubernetes clusters within DARPA to provide an execution environment for automated machine learning. MARVIN currently contains over 400 datasets and challenge problems from a wide array of ML domains including routine classification and regression to advanced video/image classification and remote sensing.