Machine learning outcomes are only as good as the data they are built on, but preparing data for these advance workloads can be time-consuming and difficult to scale, especially if you are looking to implement machine-learning applications that rely on data from across your entire organization. In this session, Ben Snively will share some best practices related to collecting, storing, and processing big data and disparate data sets so that you glean intelligent insights from your machine-learning algorithms. We will share some architectural design patterns.
