Data handling, analysis, and workflows, including collection, transformation, and practical insights.
In modern AI with LLMs, the belief persists: Bigger models are automatically better. More parameters, more compute. Yet this oversimplifies. Larger models need more data, better architecture, smart training. Often, a well-designed small model beats a big one. Smarter design trumps blind scaling.
Data quality is the hidden bottleneck in AI. Deduplication, normalization, validation, and handling missing or noisy values improve accuracy and stability. In practice, strong systems rely more on effective data cleaning than on bigger models.
