Published: 14 December 2022
Data-centric AI is a rapidly growing trend. It disrupts prevalent model-centric data science by providing solutions to improve the quality of machine learning/artificial intelligence. Cool vendors in this report offer synthetic data, data labeling and database innovations as part of data-centric AI.
Challenges such as data accessibility, volume, privacy, security, complexity and scope are among the top barriers to AI implementations. Data-centric AI solutions — like AI-specific data management, synthetic data and data labeling technologies — aim to solve many data challenges.
Across industries, synthetic data is increasingly leveraged to generate datasets that represent difficult-to-obtain data or possible future scenarios, all without privacy concerns.
Data management requirements for AI development and deployment are often missing from AI planning initiatives. Articulating clear data management and governance requirements, such as expectations for data quality and trust, lowers cost of data acquisition and helps find
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