MLOps for Scarce Image Data: A Use Case inMicroscopic Image Analysis

Nowadays, Machine Learning (ML) is experiencing tremendous popularity
that has never been seen before. The operationalization of ML models is
governed by a set of concepts and methods referred to as Machine Learning
Operations (MLOps). Nevertheless, researchers, as well as professionals, often
focus more on the automation aspect and neglect the continuous deployment
and monitoring aspects of MLOps. As a result, there is a lack of continuous
learning through the flow of feedback from production to development, causing
unexpected model deterioration over time due to concept drifts, particularly
when dealing with scarce data. This work explores the complete application
of MLOps in the context of scarce data analysis. The paper proposes a new
holistic approach to enhance biomedical image analysis. Our method includes:
a fingerprinting process that enables selecting the best models, datasets, and
model development strategy relative to the image analysis task at hand; an automated
model development stage; and a continuous deployment and monitoring
process to ensure continuous learning. For preliminary results, we perform a
proof of concept for fingerprinting in microscopic image datasets.