A Flexible Development Pipeline for Optimal Anomaly Detection in Derivative Markets

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:

Maxime Lamothe;Foutse Khomh;Heng Li

Student:

Partner:

Bourse de Montréal

Discipline:

Engineering

Sector:

Finance and Insurance

University:

Polytechnique Montréal

Program:

Accelerate

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