Putting Machine Learning into Production Systems - ACM Queue

This time around with The Morning Paper I've chosen two papers that address different aspects of putting machine learning into production systems. In "Data Validation for Machine Learning," Breck et al. share details of the pipelines used at Google to validate petabytes of production data every day. With so many moving parts it's important to be able to detect and investigate changes in data distributions before they can impact model performance. "Software Engineering for Machine Learning: A Case Study" shares lessons learned at Microsoft as machine learning started to pervade more and more of the company's systems, moving from specialized machine-learning products to simply being an integral part of many products and services. This means that software-engineering processes and practices on those projects have had to adapt.