Projects

The Adversarial Test Toolbox enables an in-depth assessment of adversarial robustness of the target models.

MLOps pipeline takes care of the ML model, including various tasks such as model training, deployment, and serving.

The confidence estimator is used to estimate the uncertainty in the predictions of the Basware distillation pipeline.

To meet the demanding requirements of MLOps IML4E has developed CABC for MLops.

A data quality dashboard for the monitoring and management of large volumes of time-series data generated by sensors and smart meters.

Studying and testing empirically different methods based on the literature review that would work especially for non-image data. 


This novel design brings significant enhancements to the field of data science, particularly in the areas of data profiling, validation, error detection, and correction.

The data quality evaluation tool conducts comprehensive assessments on the provided data, generating a holistic evaluation of its overall quality.

Discrepancy Scaling is a fast and accurate deep-learning based unsupervised anomaly detection and localization method for image data.