Microsoft: This is how to protect your machine-learning applications

Modern machine learning (ML) has become an important tool in a very short time. We're using ML models across our organisations, either rolling our own in R and Python, using tools like TensorFlow to learn and explore our data, or building on cloud- and container-hosted services like Azure's Cognitive Services. It's a technology that helps predict maintenance schedules, spots fraud and damaged parts, and parses our speech, responding in a flexible way. SEE: Prescriptive analytics: An insider's guide (free PDF) (TechRepublic) The models that drive our ML applications are incredibly complex, training neural networks on large data sets. But there's a big problem: they're hard to explain or understand.