OVERCOMING SCALABILITY CHALLENGES IN MLOps: STRATEGIES AND FUTURE DIRECTIONS

Abstract

This paper addresses the critical scalability challenges in Machine Learning Operations (MLOps), a domain pivotal for the seamless integration, deployment, and management of machine learning (ML) models in production environments. As businesses increasingly rely on ML models for decision-making and operational efficiency, the scalable deployment of these models becomes paramount. This research delves into the primary scalability issues within MLOps, including computational resource allocation, model management, continuous integration and delivery (CI/CD) pipelines, and data scalability. Through a comprehensive literature review, we identify existing strategies and gaps in the scalable implementation of MLOps practices. The paper proposes innovative solutions for overcoming these challenges, such as optimizing resource utilization, enhancing model version control, automating workflow pipelines, and managing large-scale data efficiently. Our critical analysis evaluates the effectiveness of these strategies in various operational contexts, providing insights into their practical implications and limitations. We conclude by highlighting future research directions aimed at advancing scalable MLOps frameworks, emphasizing the need for adaptive scaling strategies and exploring new architectural paradigms. This work seeks to contribute to the ongoing discourse in the field by offering a detailed exploration of scalability challenges in MLOps, proposing actionable solutions, and paving the way for future innovations.