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How to diagnose and treat machine learning models afflicted by COVID-19 | TechCrunch

The economic disruption of COVID-19 has been unprecedented in its swiftness, upsetting supply lines, temporarily closing retail stores and changing online customer behaviors. It has also dramatically increased unemployment overnight, increasing financial stress and systemic risks of both individuals and businesses. It is forecasted that global GDP could be affected by up to 0.9%, on a par with the 2008 financial crisis. While the nature of our recovery is unknown, if the 2008 crisis is any indicator, the impact of COVID-19 could be felt for years, through both short-term adjustments and long-term shifts in consumer and business behaviors and attitudes.

This disruption impacts machine learning models because the concepts and relationships the models learned when they were trained may no longer hold. This phenomenon is called “concept drift.” ML models may become unstable and underperform in the face of concept drift. That is precisely what is happening now with COVID-19. The effects of these drifts will be felt for quite some time, and models will need to be adjusted to keep up. The good news is that there have been significant developments in model intelligence technology, and through judicious use, models can nimbly adjust to those drifts.

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Source: How to diagnose and treat machine learning models afflicted by COVID-19 | TechCrunch