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Kishore will then double click into some of the opportunities we find here at Capital One, and Bayan will finish us off with a lean into one of our open-source solutions that really is an important contribution to our data-centric AI community. How are you looking at model evaluation for cases where data adapts rapidly?
Kishore will then double click into some of the opportunities we find here at Capital One, and Bayan will finish us off with a lean into one of our open-source solutions that really is an important contribution to our data-centric AI community. How are you looking at model evaluation for cases where data adapts rapidly?
Those pillars are 1) benchmarks—ways of measuring everything from speed to accuracy, to dataquality, to efficiency, 2) best practices—standard processes and means of inter-operating various tools, and most importantly to this discussion, 3) data. In order to do this, we need to get better at measuring dataquality.
Those pillars are 1) benchmarks—ways of measuring everything from speed to accuracy, to dataquality, to efficiency, 2) best practices—standard processes and means of inter-operating various tools, and most importantly to this discussion, 3) data. In order to do this, we need to get better at measuring dataquality.
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