# MLOps for Scarce Image Data: A Use Case in Microscopic Image Analysis

arXiv: https://arxiv.org/abs/2309.15521 · HTML: https://mlsanity.com/paper/2309.15521/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 39 references that are in the map.
- First posted on arXiv 27 Sep 2023.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification](https://mlsanity.com/paper/2110.14795/) (2021)
- [Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1708.08987/) (2017)
- [Machine Learning Operations (MLOps): Overview, Definition, and Architecture](https://mlsanity.com/paper/2205.02302/) (2022)
- [AutoML to Date and Beyond: Challenges and Opportunities](https://mlsanity.com/paper/2010.10777/) (2020)
- [A Data Quality-Driven View of MLOps](https://mlsanity.com/paper/2102.07750/) (2021)
- [MLOps -- Definitions, Tools and Challenges](https://mlsanity.com/paper/2201.00162/) (2022)
- [MLOps Challenges in Multi-Organization Setup: Experiences from Two Real-World Cases](https://mlsanity.com/paper/2103.08937/) (2021)
- [Task Fingerprinting for Meta Learning in Biomedical Image Analysis](https://mlsanity.com/paper/2107.03949/) (2021)
- [Few Is Enough: Task-Augmented Active Meta-Learning for Brain Cell Classification](https://mlsanity.com/paper/2007.05009/) (2020)
- [Concepts for Automated Machine Learning in Smart Grid Applications](https://mlsanity.com/paper/2110.13585/) (2021)
- [EasyMLServe: Easy Deployment of REST Machine Learning Services](https://mlsanity.com/paper/2211.14417/) (2022)

## Built on by (most cited first, 4 of 4)

- [AI-based automated active learning for discovery of hidden dynamic processes: A use case in light microscopy](https://mlsanity.com/paper/2310.04461/) (2023)
- [PDx -- Adaptive Credit Risk Forecasting Model in Digital Lending using Machine Learning Operations](https://mlsanity.com/paper/2512.22305/) (2025)
- [EAP4EMSIG -- Enhancing Event-Driven Microscopy for Microfluidic Single-Cell Analysis](https://mlsanity.com/paper/2504.00047/) (2025)
- [Self-Supervised Learning Strategies for a Platform to Test the Toxicity of New Chemicals and Materials](https://mlsanity.com/paper/2510.07853/) (2025)

## Abstract (arXiv)

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.