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Continual Learning in Practice

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arxiv 1903.05202 v2 pith:Y4MCF2EY submitted 2019-03-12 stat.ML cs.LG

Tom Diethe , Tom Borchert , Eno Thereska , Borja Balle , Neil Lawrence This is my paper · ORCID
classification stat.MLcs.LG
keywords datalearningadaptarchitecturecontinualmachinereferenceadaptive
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This paper describes a reference architecture for self-maintaining systems that can learn continually, as data arrives. In environments where data evolves, we need architectures that manage Machine Learning (ML) models in production, adapt to shifting data distributions, cope with outliers, retrain when necessary, and adapt to new tasks. This represents continual AutoML or Automatically Adaptive Machine Learning. We describe the challenges and proposes a reference architecture.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Development of an End-to-end Machine Learning System with Application to In-app Purchases

    cs.LG 2024-12 conditional novelty 4.0 of 10

    A production ML system at King predicts the day of a player's next in-app purchase, and a GRU-based model reports improved offline metrics plus a 20% A/B test lift over a rule-based baseline.

  2. HyperStream: a Workflow Engine for Streaming Data

    cs.LG 2019-08 conditional novelty 3.0 of 10

    HyperStream is a workflow engine that lets users compose streaming data pipelines with nested plates and factors, inspired by factor graphs, and execute them in online or offline mode.

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