Initial Insights on MLOps: Perception and Adoption by Practitioners

TL;DR

This paper surveys practitioners to understand how MLOps guidelines are adopted, perceived, and their impact on AI development, highlighting challenges, benefits, and future trends in real-world business contexts.

Contribution

It provides empirical insights into the adoption, perception, and challenges of MLOps guidelines across diverse organizations, informing future AI development practices.

Findings

Adoption of MLOps varies across organizations.

Practitioners face challenges in implementing MLOps.

Awareness and understanding of MLOps influence adoption rates.

Abstract

The accelerated adoption of AI-based software demands precise development guidelines to guarantee reliability, scalability, and ethical compliance. MLOps (Machine Learning and Operations) guidelines have emerged as the principal reference in this field, paving the way for the development of high-level automated tools and applications. Despite the introduction of MLOps guidelines, there is still a degree of skepticism surrounding their implementation, with a gradual adoption rate across many companies. In certain instances, a lack of awareness about MLOps has resulted in organizations adopting similar approaches unintentionally, frequently without a comprehensive understanding of the associated best practices and principles. The objective of this study is to gain insight into the actual adoption of MLOps (or comparable) guidelines in different business contexts. To this end, we surveyed practitioners representing a range of business environments to understand how MLOps is adopted and perceived in their companies. The results of this survey also shed light on other pertinent aspects related to the advantages and challenges of these guidelines, the learning curve associated with them, and the future trends that can be derived from this information. This study aims to provide deeper insight into MLOps and its impact on the next phase of innovation in machine learning. By doing so, we aim to lay the foundation for more efficient, reliable, and creative AI applications in the future.