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Data Collection & Creation
Reliable AI systems start with how data is sourced, filtered, and structured. Decisions made during collection and curation shape coverage, bias, and downstream performance long before training begins.
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As models scale, data quality becomes a limiting factor rather than model architecture. Collection and curation directly influence representativeness, edge-case coverage, and the reliability of learned behavior, especially in real-world and domain-specific deployments.
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