Building Robust Machine Learning Systems

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 worlds when building our systems? This session will take an applied view from my experience at Ravelin, and will provide useful practices and tips to help ensure your machine learning systems are robust, well audited, avoid embarrassing predictions, and introspectable, so you can hopefully sleep a little better at night.