To get AI systems off the ground, training data must be voluminous and accurately labeled and annotated. With AI becoming a growing enterprise priority, data science teams are under tremendous pressure to deliver projects but frequently are challenged to produce training data at the required scale and quality. Nearly eight out of 10 organizations engaged in AI and machine learning said that projects have stalled, according to a Dimensional Research’s Artificial Intelligence and Machine Learning Projects Obstructed by Data Issues. The majority (96%) of these organizations said they have run into problems with data quality, data labeling necessary to train AI, and building model confidence.
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