Volume and quality of training data are the largest barriers to applying machine learning
A new Alegion report highlights the difficulties companies encounter when trying to develop artificial intelligence (AI) and machine learning (ML) solutions. The survey found that almost 8 in 10 firms (78%) the have launched AI/ML projects saw their initiatives came to a grinding halt before deployment. In addition, virtually every firm (96%) has encountered serious data issues preventing them from training their algorithms successfully and doing so without exceeding the available budget.
According to Alegion CEO Nathaniel Gates, the research shows that “the single largest obstacle to implementing machine learning models into production is the volume and quality of the training data” and that companies new to AI/ML often attempt yet fail to perform data training themselves.
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