Why Machine Learning Models Crash And Burn In Production
One magical aspect of software is that it just keeps working. If you code a calculator app, it will still correctly add and multiply numbers a month, a year, or 10 years later. The fact that the marginal cost of software approaches zero has been a bedrock of the software industry’s business model since the 1980s.
This is no longer the case when you are deploying machine learning (ML) models. Making this faulty assumption is the most common mistake of companies taking their first artificial intelligence (AI) products to market. The moment you put a model in production, it starts degrading.
Why Do ML Models Fail?
Your model’s accuracy will be at its best until you start using it. It then deteriorates as the world it was trained to predict changes. This phenomenon is called concept drift, and while it’s been heavily studied in academia for the past two decades, it’s still often ignored in industry best practices. READ MORE ON: FORBES