Pipeline Optimization in MLOps: A Review of Approaches and Current Challenges

Abstract

With large-scale machine learning systems finding wide usage, challenges in the management and deployment of the same led to the development of MLOps. Issues such as resource efficiency, workflow automation, and dynamic demands are some of the challenges that arise with increasingly complex pipelines. This review outlines recent developments, placing a strong emphasis on practical solutions for pipeline optimization and simplification of deployment. Metrics-driven architectures, in addition to new frameworks, constitute better performance with processes eased. While some of the most promising solutions have evolved around model-driven evaluations and reproducibility tools, which indeed will open paths for reliable MLOps and greater efficiency, there are still a lot of open challenges concerning real-time workload shift handling and integrations with existing systems, stressing that scaling up and adaptation need appropriate strategies.