AlphaD3M: An Open-Source AutoML Library for Multiple ML Tasks

Roque Lopez, Raoni Lourenco, Remi Rampin, Sonia Castelo, Aécio S. R. Santos, Jorge Henrique Piazentin Ono, Claudio Silva, Juliana Freire

Keywords: AutoML, Python Library, Multiple ML Tasks

TL;DR: We present an open-source AutoML library that supports multiple machine learning tasks over different data types.

Abstract: We present AlphaD3M, an open-source Python library that supports a wide range of machine learning tasks over different data types. We discuss the challenges involved in supporting multiple tasks and how AlphaD3M addresses them by combining deep reinforcement learning and meta-learning to effectively construct pipelines over a large collection of primitives. To better integrate the use of AutoML within the data science lifecycle, we have built an ecosystem of tools around AlphaD3M that support user-in-the loop tasks, including the selection of suitable pipelines and the development of solutions for complex systems. We present use cases that demonstrate some of these features. We report the results of detailed experimental evaluations which show that AlphaD3M is effective and derives high-quality pipelines for a diverse set of problems with performance that is comparable or superior to state-of-the-art AutoML systems.

Abcd Fit: Applications

Submission Checklist: Yes

Broader Impact Statement: Yes

Paper Availability And License: Yes

Code Of Conduct: Yes

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GPU Hours: 0

TPU Hours: 0

Evaluation Metrics: No