Machine Learning Operations: A Mapping Study

TL;DR

This study systematically maps the challenges in MLOps pipelines and offers practical recommendations for tools and solutions to improve deployment and management of machine learning models in production.

Contribution

It provides a comprehensive mapping of MLOps challenges and suggests applicable solutions, filling a gap in understanding the complexities of deploying ML systems.

Findings

Identified key challenges in data, model, and deployment pipelines.

Mapped solutions and tools applicable across research and industry.

Highlighted the need for integrated MLOps practices.

Abstract

Machine learning and AI have been recently embraced by many companies. Machine Learning Operations, (MLOps), refers to the use of continuous software engineering processes, such as DevOps, in the deployment of machine learning models to production. Nevertheless, not all machine learning initiatives successfully transition to the production stage owing to the multitude of intricate factors involved. This article discusses the issues that exist in several components of the MLOps pipeline, namely the data manipulation pipeline, model building pipeline, and deployment pipeline. A systematic mapping study is performed to identify the challenges that arise in the MLOps system categorized by different focus areas. Using this data, realistic and applicable recommendations are offered for tools or solutions that can be used for their implementation. The main value of this work is it maps distinctive challenges in MLOps along with the recommended solutions outlined in our study. These guidelines are not specific to any particular tool and are applicable to both research and industrial settings.