BigML

In 2010, we strongly felt that it was the right time to bring Machine Learning to everyone. We wanted to design and build an accessible platform that would not only allow everybody to uncover the hidden predictive power of data with ease, but also would make the whole experience "enjoyable".

In January of 2011, we founded BigML with the mission of making Machine Learning easy and beautiful for everyone. A few years later, after many long days and nights of hard work, we are very proud of how our platform has come to help a diverse set of organizations of all sizes and industries. With BigML, they have been able to build sophisticated Machine Learning-based solutions affordably by distilling the predictive patterns from their data into real-life intelligent applications usable by anyone.

In the near future, all applications will be predictive. To stay relevant in a faster, complex, connected, and uncertain world, predictive applications will have to take advantage of Machine Learning and other Artificial Intelligence techniques in one way or another. BigML's platform, private deployments, and rich toolset will continue to help our customers create, rapidly experiment, fully automate and manage Machine Learning workflows that power best-in-class intelligent applications.

Our international team has deep expertise in the fields of Large-scale Machine Learning, Distributed Systems and Data Visualization. BigML is headquartered in Corvallis, Oregon (one of the top cities in America for innovation) and, its European headquarters are located in Valencia, Spain (one of the most beautiful cities in Europe) to harness innovation, but we never shy away from hiring wherever the talent is.

BigML is a consumable, programmable, and scalable Machine Learning platform that makes it easy to solve and automate Classification, Regression, Time Series Forecasting, Cluster Analysis, Anomaly Detection, Association Discovery, and Topic Modeling tasks. BigML is helping thousands of analysts, software developers, and scientists around the world to solve Machine Learning tasks "end-to-end", seamlessly transforming data into actionable models that are used as remote services or, locally, embedded into applications to make predictions.