# MLOps with enhanced performance control and observability

arXiv: https://arxiv.org/abs/2302.01061 · HTML: https://mlsanity.com/paper/2302.01061/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 8 references that are in the map.
- First posted on arXiv 2 Feb 2023.

## Builds on (most cited first, 2 of 2)

- [Challenges in Deploying Machine Learning: a Survey of Case Studies](https://mlsanity.com/paper/2011.09926/) (2020)
- [Who Needs MLOps: What Data Scientists Seek to Accomplish and How Can MLOps Help?](https://mlsanity.com/paper/2103.08942/) (2021)

## Abstract (arXiv)

The explosion of data and its ever increasing complexity in the last few years, has made MLOps systems more prone to failure, and new tools need to be embedded in such systems to avoid such failure. In this demo, we will introduce crucial tools in the observability module of a MLOps system that target difficult issues like data drfit and model version control for optimum model selection. We believe integrating these features in our MLOps pipeline would go a long way in building a robust system immune to early stage ML system failures.