Continual Learning in Practice

Tom Diethe, Tom Borchert, Eno Thereska, Borja Balle, Neil D Lawrence

Research output: Contribution to conferenceConference Paperpeer-review

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Abstract

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.
Original languageEnglish
Number of pages9
Publication statusPublished - 24 Apr 2019
EventContinual Learning Workshop of 32nd Conference on Neural Information Processing Systems (NeurIPS 2018) - Montreal, Quebec, Canada
Duration: 2 Dec 20188 Dec 2018
Conference number: 32
https://nips.cc/Conferences/2018

Workshop

WorkshopContinual Learning Workshop of 32nd Conference on Neural Information Processing Systems (NeurIPS 2018)
Abbreviated titleNeurIPS 2018
CountryCanada
CityMontreal, Quebec
Period2/12/188/12/18
Internet address

Bibliographical note

Presented at the NeurIPS 2018 workshop on Continual Learning https://sites.google.com/view/continual2018/home

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