# Experimentation, deployment and monitoring Machine Learning models: Approaches for applying MLOps

arXiv: https://arxiv.org/abs/2408.11112 · HTML: https://mlsanity.com/paper/2408.11112/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 20 Aug 2024.

## Builds on (most cited first, 4 of 4)

- [Challenges in Deploying Machine Learning: a Survey of Case Studies](https://mlsanity.com/paper/2011.09926/) (2020)
- [AI Lifecycle Models Need To Be Revised. An Exploratory Study in Fintech](https://mlsanity.com/paper/2010.02716/) (2020)
- [Where Is My Training Bottleneck? Hidden Trade-Offs in Deep Learning Preprocessing Pipelines](https://mlsanity.com/paper/2202.08679/) (2022)
- [Looper: An end-to-end ML platform for product decisions](https://mlsanity.com/paper/2110.07554/) (2021)

## Abstract (arXiv)

In recent years, Data Science has become increasingly relevant as a support tool for industry, significantly enhancing decision-making in a way never seen before. In this context, the MLOps discipline emerges as a solution to automate the life cycle of Machine Learning models, ranging from experimentation to monitoring in productive environments. Research results shows MLOps is a constantly evolving discipline, with challenges and solutions for integrating development and production environments, publishing models in production environments, and monitoring models throughout the end to end development lifecycle. This paper contributes to the understanding of MLOps techniques and their most diverse applications.