  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.
