Machine learning is powering huge advances in products that we know and love. As a result, ever growing parts of the systems we build are changing from the deterministic to the probabilistic. The accuracy of machine learning applications can quickly deteriorate in the wild without strategies for testing models, instrumenting their behaviour and the ability to introspect and debug incorrect predictions. Wouldn't it be nice to have the best of the software engineering and machine learning...

DSSTNE (Deep Sparse Scalable Tensor Network Engine) is a deep learning framework for working with large sparse data sets. It arose out of research into the use of deep learning for product recommendations after we realized existing frameworks were limited to a single GPU or data-parallel scaling and that they handled sparse datasets incredibly inefficiently. DSSTNE provides nearly free sparse input layers for neural networks and stores such data in a CSR-like format that allowed us to train...