In 2026, AI engineering will face significant challenges not from model development, but from data infrastructure. A key issue is training-serving skew, where the logic for preparing data for model training differs from the logic used for real-time inference, leading to model degradation and unreliable predictions. To combat this, a feature store is essential. This data system acts as an abstraction layer, managing and serving feature data consistently across batch, streaming, and online environments, ensuring that the same feature logic is used for both training and serving. AI
IMPACT Addresses critical data infrastructure needs for AI systems, aiming to improve reliability and reduce technical debt in production environments.
RANK_REASON Article discusses future infrastructure needs for AI, focusing on data management rather than model development.
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