For many organizations, AI and machine learning are seen as a route to greater efficiency and competitive advantage. But according to a new study conducted by Dimensional Research for Alegion almost eight out of 10 enterprise organizations currently engaged in AI and ML report that projects have stalled, and 96 percent of these companies have run into problems with data quality, data labeling required to train AI, and building model confidence. "The single largest obstacle to implementing machine learning models into production is the volume and quality of the training data," says Nathaniel Gates, CEO and co-founder of Alegion, a… [Continue Reading]
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