Data-Centric AI shifts focus from tweaking models to improving data quality—ensuring it’s accurate, diverse, and well-labeled. High-quality data leads to better performance than complex models trained on noisy or biased datasets. This approach emphasizes data cleaning, annotation tools, and dataset governance. In real-world applications, Data-Centric AI helps bridge the gap between academic research and practical deployment by making AI systems more robust and reliable.
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