How to Prevent Machine Learning Models from Failing in Practice?
Have you seen machine learning solutions fall flat in practice? Well, I have. Several times. I get occasional panic calls from teams about their 98% accurate models generating questionable predictions once released to actual users. Did they build a bad model? Maybe. But the real issue is that the majority of these teams skipped a step. And that step is testing. Not just any type of testing, but post-development testing (PDT). What is Post-Development Testing (PDT)? Post-development testing in the context of machine learning is an experimentation period […]
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