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Data
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Raw information from which models learn.
Data includes numbers, text, images, audio, or logs representing real-world phenomena.
- Quality aspects: Completeness, accuracy, relevance, timeliness, low bias.
- Preparation: Cleaning, enrichment, labeling, anonymization.
- Impact: Poor data → poor models; high-quality data is the strongest performance driver.