# Architecturally Significant MLOps Guidelines for ML Model Integration and Deployment: a Gray Literature Review

arXiv: https://arxiv.org/abs/2606.06535 · HTML: https://mlsanity.com/paper/2606.06535/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 3 Jun 2026.

## Builds on (most cited first, 8 of 8)

- [Guidelines for including grey literature and conducting multivocal literature reviews in software engineering](https://mlsanity.com/paper/1707.02553/) (2017)
- [Studying Software Engineering Patterns for Designing Machine Learning Systems](https://mlsanity.com/paper/1910.04736/) (2019)
- [A Meta-Summary of Challenges in Building Products with ML Components -- Collecting Experiences from 4758+ Practitioners](https://mlsanity.com/paper/2304.00078/) (2023)
- [A Large-Scale Study of Model Integration in ML-Enabled Software Systems](https://mlsanity.com/paper/2408.06226/) (2024)
- [A Multivocal Review of MLOps Practices, Challenges and Open Issues](https://mlsanity.com/paper/2406.09737/) (2024)
- [Design Patterns for AI-based Systems: A Multivocal Literature Review and Pattern Repository](https://mlsanity.com/paper/2303.13173/) (2023)
- [A Systematic Review of MLOps Tools: Tool Adoption, Lifecycle Coverage, and Critical Insights](https://mlsanity.com/paper/2604.16371/) (2026)
- [How Do Model Export Formats Impact the Development of ML-Enabled Systems? A Case Study on Model Integration](https://mlsanity.com/paper/2502.00429/) (2025)

## Abstract (arXiv)

Context. Despite the growing adoption of Machine Learning Operations (MLOps), teams often approach MLOps projects in an ad hoc manner due to the lack of consolidated architectural guidance. The community would benefit from a reference that synthesizes knowledge to inform the architectural design of MLOps systems, especially regarding the integration and deployment of ML models. Objective. In response, our goal is to provide a comprehensive overview of architecturally significant guidelines for the integration and deployment of ML models in MLOps systems. Method. We conduct a gray literature review of 103 web sources to analyze state-of-practice knowledge on MLOps model integration and deployment. We then apply thematic analysis to synthesize these practices into recommended guidelines. Results. We contribute a collection of 25 architecturally significant MLOps guidelines for model integration and deployment, organized into five categories, and describe their impact on the overall system architecture. Conclusion. Our results serve as an overview of state-of-practice MLOps guidelines to support researchers and practitioners with the integration and deployment of ML models in their MLOps systems.