MLOps best practices, challenges and maturity models: A systematic literature review

Abstract

Context:

Agile and DevOps methodologies have revolutionized software development, leading to increased efficiency and reliability in product delivery. Building on this success, Machine Learning Operations (MLOps) has emerged to streamline the development and deployment of machine learning (ML) models, addressing challenges unique to ML workflows.

Objectives:

This study aims to explore the complexities organizations face when adopting MLOps, focusing on three main challenges: the lack of standardized practices, difficulties in maintaining model consistency and scalability, and ambiguities in assessing MLOps maturity. The study also aims to identify best practices and common pitfalls, contributing to a clearer understanding and standardization of MLOps.

Methods:

A comprehensive literature review was conducted, analyzing 45 articles that address MLOps best practices, challenges, maturity models, and lessons from previous implementations. This review categorizes findings to provide insights into successful and unsuccessful MLOps applications.

Results:

The study identifies nine best practices, eight common challenges, and five maturity models relevant to MLOps adoption. Key lessons from successful and unsuccessful MLOps implementations are outlined, with a focus on improving standardization and reducing ambiguity in MLOps practices.

Conclusion:

The findings highlight the importance of establishing standardized MLOps practices to address the unique challenges of machine learning workflows. By categorizing best practices, maturity models, and lessons learned, this study aims to contribute to a robust MLOps framework that enhances the reliability and scalability of machine learning in production environments.