Why businesses using machine learning should not ignore concept drift

BUSINESSES often think that machine learning (ML) models learn on their own and get better over time. That’s not true.If organizations want to use the technology effectively in 2020, they need to understand why and what to do about it.Business leaders have been told that they need a mountain of data to train any artificial intelligence (AI) or machine learning model. As a result, much of their efforts in the past year have been focused on acquiring data.However, once the models are deployed, they stop evolving and fail to account for changes that occur in variables. As a result, over time, ML models slowly start becoming inaccurate. This is known as concept drift and is something that academia has been studying for quite a while but businesses seem to have been ignoring.In the case of concept drift, our interpretation of the data changes with time even while the general distribution of the data does not, said Phillips Strategy and Innovation Consultant (and former AI Advisor) Alexandre Gonfalonieri.This causes the end-user to interpret the model predictions as having deteriorated over time for the same/similar data. Both data and concept can simultaneously drift as well, further vexing the matters.