How Data Teams Handle Model Uncertainty
transcript
show notes
Most data teams treat model predictions as facts, but uncertainty is the real currency of decision-making. In this episode, we look at how leading organizations use conformal prediction to quantify risk rather than just accuracy. We break down why a ninety-five percent confidence interval matters more than a point estimate when you are deploying machine learning models in production. Lucas and Luna discuss the practical steps for implementing these methods without slowing down your development cycle, using concrete examples from logistics and healthcare. This is not about building bigger models; it is about knowing what you do not know.
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