Case Study: Philips achieves faster, self-documenting ML experiments with ClearML

Self-Documenting ML Experiments Slash Wasted Time for Philips

Philips Radiology Informatics, part of Philips, needed a better way to manage machine learning experiments in a highly competitive medical imaging environment. Developers were spending too much time manually documenting experiments, and meetings were slowed by poor transparency and the lack of easy access to past work. They turned to ClearML’s experiment management module to improve documentation, collaboration, and workflow efficiency.

ClearML helped automate experiment tracking by capturing metrics, parameters, and TensorBoard data with very little code, while also providing built-in visualization, search, filtering, and a standardized API and UI for managing experiments. As a result, Philips reduced wasted time, eliminated many update meetings, and made reviews faster and more data-driven. The team reported hours saved, especially when retrieving and comparing past experiments, and ClearML significantly improved productivity across the algorithm team.

Philips

Evi Kopelowitz

Algorithm Developer