Projects

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

An evaluation framework that supports optimization of machine learning deployments for inference performance.

The ML lineage is a framework to holistically capture and connect the required information about ML model development and operations. 

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

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

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

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

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

A Toolkit to capture information related to the ML models, such as intended use cases and the limitation of the ML model.