Amazon SageMaker Autopilot: a white box AutoML solution at scale

• URL: http://arxiv.org/abs/2012.08483v2
• Date: Wed, 16 Dec 2020 18:51:27 GMT
• Title: Amazon SageMaker Autopilot: a white box AutoML solution at scale
• Authors: Piali Das, Valerio Perrone, Nikita Ivkin, Tanya Bansal, Zohar Karnin, Huibin Shen, Iaroslav Shcherbatyi, Yotam Elor, Wilton Wu, Aida Zolic, Thibaut Lienart, Alex Tang, Amr Ahmed, Jean Baptiste Faddoul, Rodolphe Jenatton, Fela Winkelmolen, Philip Gautier, Leo Dirac, Andre Perunicic, Miroslav Miladinovic, Giovanni Zappella, C\'edric Archambeau, Matthias Seeger, Bhaskar Dutt, Laurence Rouesnel
• Abstract summary: We present Amazon SageMaker Autopilot, a fully managed system providing an automated machine learning solution. Autopilot identifies the problem type, analyzes the data and produces a diverse set of complete ML pipelines. In the scenario where the performance is not satisfactory, a data scientist is able to view and edit the proposed ML pipelines.
• Score: 16.607423930188734
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: AutoML systems provide a black-box solution to machine learning problems by selecting the right way of processing features, choosing an algorithm and tuning the hyperparameters of the entire pipeline. Although these systems perform well on many datasets, there is still a non-negligible number of datasets for which the one-shot solution produced by each particular system would provide sub-par performance. In this paper, we present Amazon SageMaker Autopilot: a fully managed system providing an automated ML solution that can be modified when needed. Given a tabular dataset and the target column name, Autopilot identifies the problem type, analyzes the data and produces a diverse set of complete ML pipelines including feature preprocessing and ML algorithms, which are tuned to generate a leaderboard of candidate models. In the scenario where the performance is not satisfactory, a data scientist is able to view and edit the proposed ML pipelines in order to infuse their expertise and business knowledge without having to revert to a fully manual solution. This paper describes the different components of Autopilot, emphasizing the infrastructure choices that allow scalability, high quality models, editable ML pipelines, consumption of artifacts of offline meta-learning, and a convenient integration with the entire SageMaker suite allowing these trained models to be used in a production setting.

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