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Machine Learning
Why Data Quality Matters More Than Model Hype
Training data shapes the behavior your model learns, the mistakes it makes, and the confidence people can place in it.
By RaydyantRaydyant Insights
More data is not automatically better data. Useful training examples are relevant, consistent, representative, and labeled in a way that matches the real task.
Create a data review process and an evaluation set before training. That gives the team a way to see whether improvements are real rather than accidental.
Good digital work keeps learning after launch. The ideas in this article are starting points for a conversation about the people, workflows, and outcomes behind your product.
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