Labeling tools are great, but what about quality checks?

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Modern datasets contain hundreds of thousands to millions of labels that must be kept accurate. In practice, some errors in the dataset average out and can be ignored – systematic biases transfer to the model. After quick initial wins in areas where abundant data is readily available, deep learning needs to become more data efficient to help solve difficult business problems.