Recent research by Ribeiro, Singh and Guestrin at the University of Washington provides a framework to explain why machine learning models make a particular predictions, and how even non-experts can use the explanations to improve the performance of a model. Companies must be confident that their models are achieving the desired outcomes and understand how the predictions are made.
Industry adoption of machine learning requires trust in models and predictions among scientists, developers and line of business managers.
“We are excited to see Dato working on this new initiative.” With Dato’s machine learning platform, companies can gain trust and confidence in the models and predictions behind their core business applications.Īt Capital One, we exhaustively work on model robustness and validation,” said Brendan Herger, Capital One Data Innovation Lab Data Scientist. The need for trust in models and predictions is an indicator of market adoption among established companies.”ĭato introduced tools within GraphLab Create and Dato Predictive Services to build trust and confidence in machine learning by making it easy to evaluate, explore, and explain models and predictions. “We have more than 80 commercial customers. Dato is the first machine learning company to address the industry need for confidence in models and predictions.ĭemand for machine learning has spread to large enterprise organizations,” said Carlos Guestrin, Dato CEO and Amazon Professor of Machine Learning at University of Washington. Dato, creator of the popular machine learning platform GraphLab Create, announced tools to give scientists, developers and users confidence in machine learning models and predictions.
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