Machine learning will have a profound impact on business. But machine learning models are only as good as the data used to build them. For machine learning to be highly accurate, it requires algorithms that have been trained and refined on very large sets of data. Data scientists are faced with an unwanted tradeoff on deciding which data to sample – because it is often impractical to move it all to a central location.
Overcome the problems associated with having to sample data so you can develop highly accurate machine learning applications. Train and refine machine learning algorithms on a complete, continuous dataset applied directly against a geographically distributed database. Apply machine learning to improve business operations such as predictive maintenance, sales forecasting, inventory management and customer support. Reduce risk and lost revenue by applying to security analysis and for identifying fraudulent transactions.