Avoid the metrics trap: Get the real AI performance story

We all want to know how our AI models are performing—and accuracy gains should be cause to celebrate. But how can you tell if those gains are the result of solving real issues? Did you fix a tough problem, or just get a little bit better at the easier stuff?

Red Hat Senior Principal Product Manager William Caban shows how to break down your metrics into categories so you can see clearly beyond the aggregate scores and track changes that make substantial differences to AI performance.

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