SEATTLE--(BUSINESS WIRE)--Dato, creator of the popular machine learning platform GraphLab Create (GLC), announced today tools to give scientists, developers and users confidence in machine learning models and predictions. Dato is the first machine learning company to address the industry need for confidence in models and predictions.
“Demand for machine learning has spread to large enterprise organizations,” said Carlos Guestrin, Dato CEO and Amazon Professor of Machine Learning at University of Washington. “We have more than 80 commercial customers. The need for trust in models and predictions is an indicator of market adoption among established companies.”
Dato introduced tools within GraphLab Create to build trust and confidence in machine learning by making it easy to evaluate, explore, and explain models and predictions. With Dato’s machine learning platform, companies can gain trust and confidence in the models and predictions behind their core business applications.
"At Capital One, we exhaustively work on model robustness and validation,” said Brendan Herger, Capital One Data Innovation Lab Data Scientist. "We are excited to see Dato working on this new initiative."
Industry adoption of machine learning requires trust in models and predictions among scientists, developers and line of business managers. Companies must be confident that their models are achieving the desired outcomes and understand how the predictions are made. Recent research by Ribeiro, Singh and Guestrin at the University of Washington provides a framework to explain why machine learning models make a particular prediction, and how even non-experts can use the explanations to improve the performance of a model.
Evaluation, Exploration, Explanation
The confidence-building tools included in GLC address the need to evaluate, explore and explain machine learning models and predictions.
- Evaluations let users quantitatively measure the quality of their models and predictions and compare alternative methods, through a variety of visual and numeric cues.
- Explorations expose the predictions made by learned models, allowing developers to experience what users of their intelligent applications will experience.
- Explanations shed light onto why a model makes a particular prediction, allowing developers to gain confidence that models are making decisions for the right reason.
The confidence-building tools make the path from inspiration to production easier, faster and more trustworthy. For example, a developer who wants to add a recommendation engine into an application can use the GLC Recommendation toolkit to build the model in minutes, immediately evaluate its efficacy quantitatively, explore the resulting predictions to understand what items will be recommended and provide users with explanations of why those recommendations have been made to gain their confidence in the developed system. Similarly, the GLC Churn Prediction toolkit will not only output a ranked list of customers who are likely to churn, but also reasons why the customers are likely to churn. The explanations become the drivers for business activities, such as marketing campaigns.
Machine learning technologies are at the core of a new generation of intelligent applications that differentiate disruptive businesses from established players. Today, business tasks like product recommendation, image tagging, sentiment analysis, churn prediction, fraud detection and lead scoring can only be achieved using machine learning. To build these applications at scale, companies are adopting Dato GLC, enabling developers to accelerate the innovation cycle, and quickly take their ideas from inspiration to production.
Pricing and availability
Dato is the company behind GLC, the fastest and most complete platform for building intelligent applications using machine learning technology. Dato’s commercial products are used by many Fortune 500 firms, notable retailers and service providers for fraud detection, to make recommendations, score marketing content and generally deliver predictive capabilities.
For more information, visit https://dato.com.