MLOps: A Review

TL;DR

This paper reviews the current state of MLOps, analyzing existing methods and tools, highlighting their strengths and limitations, and emphasizing the need for more autonomous, self-regulating solutions in machine learning operations.

Contribution

It provides a comprehensive review of 22 studies on MLOps, evaluating their features and usability, and discusses the gap in fully autonomous MLOps methods.

Findings

Limited fully autonomous MLOps solutions exist.

Assessment of various MLOps tools and their operability.

Highlighting the need for self-regulating MLOps advancements.

Abstract

Recently, Machine Learning (ML) has become a widely accepted method for significant progress that is rapidly evolving. Since it employs computational methods to teach machines and produce acceptable answers. The significance of the Machine Learning Operations (MLOps) methods, which can provide acceptable answers for such problems, is examined in this study. To assist in the creation of software that is simple to use, the authors research MLOps methods. To choose the best tool structure for certain projects, the authors also assess the features and operability of various MLOps methods. A total of 22 papers were assessed that attempted to apply the MLOps idea. Finally, the authors admit the scarcity of fully effective MLOps methods based on which advancements can self-regulate by limiting human engagement.