Data-centric AI is a growing movement that shifts the technical focus in AI systems from the model to the data. However, data-centric AI faces many outstanding challenges, including measuring data quality, data iteration and engineering data as part of the ML project workflow, data management tools, crowdsourcing, data augmentation and data synthesis, as well as responsible AI. This talk identifies the key pillars of data-centric AI, identifies the trends in the data-centric AI movement, and outlines a vision for taking ideas applied intuitively by a handful of experts and synthesizing them into tools that make application systematic for everyone .

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