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Data-centric AI is an approach that focuses on enhancing and enriching training data to drive better AI outcomes, as opposed to a model-centric approach wherein AI outcomes are driven by model tuning. Data-centric AI also addresses data quality, privacy and scalability. Quality data is crucial for generative AI to perform well on specific tasks. Examples of data-centric AI include knowledge graphs, feature stores, synthetic data, data labeling and annotation, and federated ML.
Read: What's New in the 2023 Gartner Hype Cycle for AI
Learn: Understanding Gartner Hype Cycles
Explore: Featured Business Insights and Trends
Listen: The AI Hype Cycle 2023: New Technologies on the Innovation Trigger
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