Machine-learning models trained on pre-COVID data are now completely out of whack, says Gartner

Machine learning models built for doing business prior to the COVID-19 pandemic will no longer be valid as economies emerge from lockdowns, presenting companies with new challenges in machine learning and enterprise data management, according to Gartner.

The research group has reported that “the extreme disruption in the aftermath of COVID-19… has invalidated many models that are based on historical data.”

Organisations commonly using machine learning for product recommendation engines or next-best-offer, for example, will have to rethink their approach. They need to broaden their machine learning techniques as there is not enough post-COVID-19 data to retrain supervised machine learning models.

Advanced modelling techniques can help

In any case the ‘new normal’ is still emerging, making the validity of prediction models a challenge, said Rita Sallam, distinguished research vice president at Gartner.

“It’s a lot harder to just say those models based on typical data that happened prior to the COVID-19 outbreak, or even data that happened during the pandemic, will be valid. Essentially what we’re seeing is [a] complete shift in many ways in customer expectations, in their buying patterns. Old processing, products, customer needs and wants, and even business models are being replaced. Organisations have to replace them at a pace that is just unprecedented,” she said.

Source: Machine-learning models trained on pre-COVID data are now completely out of whack, says Gartner • The Register