MLOPs – Toolchain Update

The management of the lifecycle of artificial intelligence (AI) models—commonly referred to as MLOps – has become a major concern for organizations aiming to deploy AI at scale. The MLOps process holds great promise for accelerating AI development and demonstrating its robustness and reliability before deploying it on critical systems such as drones. The expected outcomes of this project are numerous: increased productivity for AI experts, faster deployment of innovative solutions, reduced development and production time for AI algorithms by shortening delays between AI research teams and software development teams, and improved competitiveness for companies (such as Thales) through optimized MLOps process management. Furthermore, the success of this project will not only benefit Thales but also strengthen Canada’s position as a leader in innovation and technological development in key areas such as artificial intelligence. Additional academic collaborations are planned for the development of specific tools that will be essential and should be integrated into an MLOps chain associated with AI for critical systems.

Faculty Supervisor:

Pascal Germain

Student:

Partner:

Thales Canada Inc (Montreal, QC)

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Université Laval

Program: