Ben Snively is a Principal Solutions Architect in Data Science for Amazon Web Services (AWS), where he specializes in building systems and solutions leveraging Big Data, Analytics, Machine Learning, and Deep Learning. Ben has over 15 years working in the analytics and machine learning space and helps bridge the gap between technology and business initiatives. He holds both a Masters of Science in Computer Science from Georgia Institute of Technology and a Masters in Science in Computer Engineering from University of Central Florida
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.