[D] Rethinking the Importance of Data Quality in Machine Learning

In the machine learning world, data has often been overlooked in favor of “improved” model architectures. We must recognize that data is as important as the model.

* Garbage In, Garbage Out: Even the most sophisticated model is useless with messy, incomplete, or biased data. Focusing on data quality ensures the model learns from the right information.
* Unlocking Model Potential: Cleaner, richer data allows models to reach their full potential. Imagine a high-performance car running on low-grade fuel - …

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