Enhancing Cell Counting through MLOps: A Structured Approach for Automated Cell Analysis
It addresses operational inefficiencies for researchers and laboratory professionals in fields like neuroscience and medicine, offering incremental improvements through structured MLOps principles.
This paper tackles the challenge of effectively implementing machine learning models for cell counting by introducing CC-MLOps, a framework that streamlines integration into workflows, resulting in enhanced model reliability, reduced human error, and scalable solutions.
Machine Learning (ML) models offer significant potential for advancing cell counting applications in neuroscience, medical research, pharmaceutical development, and environmental monitoring. However, implementing these models effectively requires robust operational frameworks. This paper introduces Cell Counting Machine Learning Operations (CC-MLOps), a comprehensive framework that streamlines the integration of ML in cell counting workflows. CC-MLOps encompasses data access and preprocessing, model training, monitoring, explainability features, and sustainability considerations. Through a practical use case, we demonstrate how MLOps principles can enhance model reliability, reduce human error, and enable scalable Cell Counting solutions. This work provides actionable guidance for researchers and laboratory professionals seeking to implement machine learning (ML)- powered cell counting systems.