# Machine Learning Operations (MLOps): Overview, Definition, and Architecture

**Authors:** Dominik Kreuzberger, Niklas Kühl, Sebastian Hirschl  
**arXiv:** [2205.02302](https://arxiv.org/abs/2205.02302)  
**Date:** 2022-05-04  
**Summary page:** https://www.summarizepaper.com/en/paper/2205.02302v1/

## Key points

- The paper addresses the challenges in automating and operationalizing machine learning (ML) products.
- The concept of Machine Learning Operations (MLOps) is introduced as a solution.
- MLOps encompasses best practices, concepts, and development culture.
- Mixed-method research techniques were used to investigate MLOps implementation.
- The paper provides an overview of fundamental principles, components, roles, architecture, and workflows for successful MLOps implementation.
- A clear definition of MLOps is provided along with open challenges in the field.
- The findings serve as valuable guidance for researchers and practitioners in automating and operating ML products.
- Following the outlined principles and leveraging appropriate tools can improve project success.

## Summary

The paper titled "Machine Learning Operations (MLOps): Overview, Definition, and Architecture" by Dominik Kreuzberger, Niklas Kühl, and Sebastian Hirschl addresses the challenges faced in automating and operationalizing machine learning (ML) products. The ultimate goal of industrial ML projects is to develop ML products and bring them into production quickly. However, many ML endeavors fail due to the difficulty in automating and operationalizing these products. To address this issue, the authors introduce the concept of Machine Learning Operations (MLOps). MLOps encompasses various aspects such as best practices, sets of concepts, and development culture. To bridge the gap between theory and practice regarding MLOps implementation, the authors employ mixed-method research techniques including literature review, tool review, and expert interviews. Through their investigations they provide an aggregated overview of the fundamental principles, components, roles architecture and workflows necessary for successful MLOps implementation. Additionally they offer a clear definition of MLOps while highlighting open challenges in the field. The findings of this work serve as valuable guidance for researchers and practitioners seeking to automate and operate their ML products using specific technologies. By following the outlined principles and leveraging appropriate tools within a defined framework for MLOps implementation researchers can improve their chances of successfully delivering projects that meet expectations. Overall this paper contributes to a better understanding of MLOps by providing a comprehensive overview of its key elements along with practical insights for its implementation.