# Towards Automating the AI Operations Lifecycle

arXiv: https://arxiv.org/abs/2003.12808 · HTML: https://mlsanity.com/paper/2003.12808/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2020.

## Built on by (most cited first, 2 of 2)

- [How Much Automation Does a Data Scientist Want?](https://mlsanity.com/paper/2101.03970/) (2021)
- [The Pipeline for the Continuous Development of Artificial Intelligence Models -- Current State of Research and Practice](https://mlsanity.com/paper/2301.09001/) (2023)

## Abstract (arXiv)

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.