Most ML projects fail to reach production. Five recurring pitfalls drive failures in ML projects: choosing the wrong problem, data quality/labeling issues, the model-to-product gap, offline-online ...

Robust, deployable and collaborative machine learning (ML) methods are needed for AI to become truly useful. This ERC-funded research aims to solve a major ML bottleneck and will form a cornerstone of ...

The ability to anticipate what comes next has long been a competitive advantage -- one that's increasingly within reach for developers and organizations alike, thanks to modern cloud-based machine ...

Data volumes continue to explode with the global “datasphere” – the total amount of data created, captured, replicated and consumed – growing at more than 20 percent a year to reach approximately 291 ...