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.
@article{arxiv.2003.12808,
title = {Towards Automating the AI Operations Lifecycle},
author = {Matthew Arnold and Jeffrey Boston and Michael Desmond and Evelyn Duesterwald and Benjamin Elder and Anupama Murthi and Jiri Navratil and Darrell Reimer},
journal= {arXiv preprint arXiv:2003.12808},
year = {2020}
}