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