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Active-Learning-as-a-Service: An Automatic and Efficient MLOps System for Data-Centric AI

arXiv 2207.09109 ↗First posted 19 Jul 2022cs.LG cs.DCcited by 0

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Abstract (arXiv)

The success of today's AI applications requires not only model training (Model-centric) but also data engineering (Data-centric). In data-centric AI, active learning (AL) plays a vital role, but current AL tools 1) require users to manually select AL strategies, and 2) can not perform AL tasks efficiently. To this end, this paper presents an automatic and efficient MLOps system for AL, named ALaaS (Active-Learning-as-a-Service). Specifically, 1) ALaaS implements an AL agent, including a performance predictor and a workflow controller, to decide the most suitable AL strategies given users' datasets and budgets. We call this a predictive-based successive halving early-stop (PSHEA) procedure. 2) ALaaS adopts a server-client architecture to support an AL pipeline and implements stage-level parallelism for high efficiency. Meanwhile, caching and batching techniques are employed to further accelerate the AL process. In addition to efficiency, ALaaS ensures accessibility with the help of the design philosophy of configuration-as-a-service. Extensive experiments show that ALaaS outperforms all other baselines in terms of latency and throughput. Also, guided by the AL agent, ALaaS can automatically select and run AL strategies for non-expert users under different datasets and budgets. Our code is available at urlhttps://github.com/MLSysOps/Active-Learning-as-a-Service.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 27 of its 58 references that are in the map. First posted on arXiv 19 Jul 2022.

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References · 27

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15 more
  1. 2019Ludwig: a type-based declarative deep learning toolboxcited by 24
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  15. 2021Automatic Knowledge Augmentation for Generative Commonsense Reasoningcited by 1

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