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Continuous Monitoring and Improvement for Trustworthy AI: A Case Study on content classifiers – WestOps
Ouest France, a leading media company, uses Machine Learning (ML) techniques for content classification, information extraction, and recommendation. Ouest-France is exploring new approaches for MLOps implementation to streamline the evaluation process, ensure timely detection of performance variatio
Adaptive Machine Learning Operations – AdaptiveMLOps
By extending DevOps principles to Data Sciences and Machine Learning, MLOps provides tools for the training, deployment and operation of AI models as ordinary software components that compose software architectures. A key issue of these processes is the continuous training of AI models to adapt them
BenchArk - An efficient and robust benchmarking suite for AI – BenchArk
Numerical evaluation of novel methods, a.k.a. benchmarking, is a pillar of the scientific method in machine learning. However, due to practical and statistical obstacles, the reproducibility of published results is currently insufficient: many details can invalidate numerical comparisons, from insu
Incorporating FATES Principles in Continuous Development of ML-Integrated Systems: A MLOps Perspective – FATES-MLOps
The MLOps movement adopts the DevOps objective of reducing the gaps between development and operations teams by integrating data scientist teams and Machine Learning (ML) models. In this project, we wish to apply and adapt good software engineering practices to strengthen both the overall quality
Operationalizing federated learning in real-world healthcare applications – Fed-Ops
The practical use of federated learning (FL) in real-world healthcare applications has been so far limited in scope and extent, essentially by focusing in showcasing the feasibility of FL in ad-hoc analysis based on pre-defined models and datasets. We are in dire need for solutions to allow the long
