DESIGN AND IMPLEMENTATION OF SCALABLE MACHINE LEARNING OPERATIONS (MLOPS) PIPELINES USING DEVOPS PRINCIPLES

Abstract

The rapid integration of Machine Learning (ML) technologies into modern IT systems has amplified the need for automated and scalable solutions to manage the ML lifecycle. Machine Learning Operations (MLOps) has emerged as a framework that bridges the gap between model development and deployment, ensuring seamless integration, monitoring, and maintenance of ML applications in production environments. This paper explores the fundamental principles, methodologies, and components of MLOps, providing an in-depth review of current platforms and tools available for building ML pipelines. We present a novel approach to constructing an end-to-end MLOps pipeline utilizing open-source libraries and DevOps practices. Our proposed pipeline emphasizes continuous integration, deployment, and monitoring, enabling rapid iterations and adaptability to evolving data landscapes. The results demonstrate the effectiveness of the designed pipeline in automating workflows, improving model reproducibility, and maintaining performance in real-world applications.