ASML: A Scalable and Efficient AutoML Solution for Data Streams

Nilesh Verma, Albert Bifet, Bernhard Pfahringer, Maroua Bahri

Keywords: Online AutoML, AutoML for Data Stream, Data Stream, AutoML

Abstract: Online learning poses a significant challenge to AutoML, as the best model and configuration may change depending on the data distribution. To address this challenge, we propose Automated Streaming Machine Learning (ASML), an online learning framework that automatically finds the best machine learning models and their configurations for changing data streams. It adapts to the online learning scenario by continuously exploring a large and diverse pipeline configuration space. It uses an adaptive optimisation technique that utilizes the current best design, adaptive random directed nearby search, and an ensemble of best performing pipelines. We experimented with real and synthetic drifting data streams and showed that ASML can build accurate and adaptive pipelines by constantly exploring and responding to changes. In several datasets, it outperforms existing online AutoML and state-of-the-art online learning algorithms.

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Submission Number: 13