Machine Learning Operations: A Survey on MLOps Tool Support

TL;DR

This survey reviews current MLOps tools, analyzing their features, performance, and use cases, highlighting the need for more comprehensive platforms to automate ML workflows effectively.

Contribution

It provides a comprehensive comparison of existing MLOps tools, identifying gaps and challenges in automation and platform completeness for ML project deployment.

Findings

MLOps tools vary significantly in features and usability.

Most tools lack full automation capabilities.

There is a shortage of fully functional MLOps platforms.

Abstract

Machine Learning (ML) has become a fast-growing, trending approach in solution development in practice. Deep Learning (DL) which is a subset of ML, learns using deep neural networks to simulate the human brain. It trains machines to learn techniques and processes individually using computer algorithms, which is also considered to be a role of Artificial Intelligence (AI). In this paper, we study current technical issues related to software development and delivery in organizations that work on ML projects. Therefore, the importance of the Machine Learning Operations (MLOps) concept, which can deliver appropriate solutions for such concerns, is discussed. We investigate commercially available MLOps tool support in software development. The comparison between MLOps tools analyzes the performance of each system and its use cases. Moreover, we examine the features and usability of MLOps tools to identify the most appropriate tool support for given scenarios. Finally, we recognize that there is a shortage in the availability of a fully functional MLOps platform on which processes can be automated by reducing human intervention.