Enhancing the reliability and efficiency of MLOPs tools in industrial settings

Thales is a global technology leader investing in digital and “deep tech” innovations – Big Data, artificial intelligence, connectivity, cybersecurity and quantum technology – to build a future we can all trust. This internship is part of the Trustworthy & Efficient AI stream described in Umbrella Part 2: focusing on enhancing safety robustness, understanding, and security of deep learning models for critical and autonomous systems. Current MLOps technologies are often general, therefore not fitting the specific needs of Thales; or proprietary which is unsuitable for some of the sensitive technologies at Thales. This project aims to target issues surrounding the automatic evaluation of annotation techniques, automating the selection of ML models optimized for resource-constrained environments and the modularization of MLOps pipelines. Thales collaborates closely with Canadian public safety organizations such as the Canadian Space Agency, Defense Research and Development Canada, etc. for making any advancements achieved by Thales a direct benefit to Canada. Not only do these collaborations enhance Thales’s capabilities, but they also contribute significantly to the advancement of Canadian interests. The success of this project not only benefits Thales but also strengthens Canada’s position at the forefront of innovation and technological development in crucial domains.

Faculty Supervisor:

Maxime Lamothe

Student:

Partner:

Thales Recherche et Technologie

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Polytechnique Montréal

Program: