MLOps critical success factors - A systematic literature review

Abstract

MLOps encompasses a collection of practices integrating machine learning into operational activities, a recent addition to the diverse array of machine learning process models. The need to tightly integrate machine learning with information systems operations to ensure organizational performance led to the development of this approach. Therefore, MLOps methodologies are useful for businesses that want to make their ML operations and procedures more efficient. The purpose of this study is to summarize the many critical success factors that have been identified in studies focusing on MLOps initiatives. The paper shows how these CSFs affect MLOps performance and what factors drive this influence. We picked primary papers for analysis after conducting searches in three major publishing databases. We narrowed the field down to 58 unique CSFs, which were then classified according to three dimensions: technical, organizational, social and cultural. These CSFs affect and drive performance in MLOps, based on the results of the literature review. Researchers and industrial experts may enhance their understanding of CSFs and get insights into tackling MLOps difficulties inside organizations. The paper, notably, emphasizes several prospective research directions linked to CSFs.