Towards Automating the AI Operations Lifecycle
This addresses the need for more efficient AI deployment for practitioners, but it appears incremental as it builds on existing operational frameworks.
The paper tackles the problem of high human effort in AI operations by presenting enabling technologies for automation, focusing on performance prediction and KPI analysis to reduce manual involvement in stages like testing and monitoring.
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.