Results

The MLOps platform is made to handle a large number of models to automate training, retraining and serving those models.

The IML4E framework aims to bring an end-to-end approach on working with ML in enterprises.

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

A comprehensive framework that incorporates testing in all phases by combining classical software engineering with data science activities

The IML4E Maturity Assessment helps to examine the implementation of MLOps in an organization and make informed decisions about improvements.

Pipeline Probe is a novel, open-source tool designed for automated quality assessment of machine learning models within MLOps pipelines.

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

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

ML-Py-Stevedore is a ML-framework agnostic wrapper class and generic API plus Podman/Colima/Docker build automation for Python ML models.

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

VALICY is an AI based python framework that enables virtual validation for AI/ML (and also complex software) applications.

SAGED is a tool to automatically identify the contaminated records in tabular data in a holistic manner.

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


A typology for categorizing metrics for ML systems, and an initial list of AI ethics metrics for each category.

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