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      Towards Automating the AI Operations Lifecycle

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          Abstract

          Today's AI deployments often require significant human involvement and skill in the operational stages of the model lifecycle, including pre-release testing, monitoring, problem diagnosis and model improvements. We present a set of enabling technologies that can be used to increase the level of automation in AI operations, thus lowering the human effort required. Since a common source of human involvement is the need to assess the performance of deployed models, we focus on technologies for performance prediction and KPI analysis and show how they can be used to improve automation in the key stages of a typical AI operations pipeline.

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          Author and article information

          Journal
          28 March 2020
          Article
          2003.12808
          79dea944-6119-4e65-a5fc-623f1f15f2db

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

          Custom metadata
          cs.LG cs.SE

          Software engineering,Artificial intelligence
          Software engineering, Artificial intelligence

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