# A Multivocal Review of MLOps Practices, Challenges and Open Issues

arXiv: https://arxiv.org/abs/2406.09737 · HTML: https://mlsanity.com/paper/2406.09737/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2024.

## Built on by (most cited first, 5 of 5)

- [Towards Secure MLOps: Surveying Attacks, Mitigation Strategies, and Research Challenges](https://mlsanity.com/paper/2506.02032/) (2025)
- [A Category Theory Account of AI Identity](https://mlsanity.com/paper/2607.00220/) (2026)
- [Update Opacity: Epistemic Accessibility and Governance Under AI System Change](https://mlsanity.com/paper/2606.00037/) (2026)
- [Architecturally Significant MLOps Guidelines for ML Model Integration and Deployment: a Gray Literature Review](https://mlsanity.com/paper/2606.06535/) (2026)
- [Trustworthy Data- and ML-Ops for Intelligent Transportation Systems and Logistics](https://mlsanity.com/paper/2610.01282/) (2026)

## Abstract (arXiv)

MLOps has emerged as a key solution to address many socio-technical challenges of bringing ML models to production, such as integrating ML models with non-ML software, continuous monitoring, maintenance, and retraining of deployed models. Despite the utility of MLOps, an integrated body of knowledge regarding MLOps remains elusive because of its extensive scope due to the diversity of ML productionalization challenges it addresses. Whilst the existing literature reviews provide valuable snapshots of specific practices, tools, and research prototypes related to MLOps at various times, they focus on particular facets of MLOps, thus fail to offer a comprehensive and invariant framework that can weave these perspectives into a unified understanding of MLOps. This paper presents a Multivocal Literature Review that systematically analyzes a corpus of 150 peer-reviewed and 48 grey literature to synthesize a unified conceptualization of MLOps and develop a snapshot of its best practices, adoption challenges, and solutions.