Data Drift Detection and Monitoring

When a previously trained machine learning model is put into production, the production phase begins where said model makes predictions on the inputs provided to it. When the distribution of production data changes over time, we talk about data drift. Then the model is likely to become less efficient, or even obsolete. The project consists of building an intelligent system capable of alerting in the event of a data drift that would have a significant impact on the system.

Faculty Supervisor:

Heng Li;Foutse Khomh;Mohammad Hamdaqa;Maxime Lamothe

Student:

Partner:

Zelros;Zelros (France)

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Polytechnique Montréal

Program:

Accelerate

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