For many years, businesses and their data science teams valued accuracy above all else when it came to a model’s performance. Increasingly, however, other factors and trade-offs have come into play depending on the business context of models. From biases buried in training data to the costs associated with runtime and resourcing services underpinned by … continue reading
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