Slides: https://www.slideshare.net/DataStax/c-for-deep-learning-andrew-jefferson-tracktable-cassandra-summit-2016 | A deep learning startup has a requirement for a robust and scalable data architecture. Training a Deep Neural Network requires 10s-100s of millions of examples consisting of data and metadata. In addition to training it is necessary to support test/validation, data exploration and more traditional data science analytics workloads. As a startup we have minimal resources and an engineering team of 1. Cassandra, Spark and Kafka running on Mesos in AWS is a scalable architecture that is fast and easy to set up and maintain to deliver a data architecture for Deep Learning. About the Speaker Andrew Jefferson VP Engineering, Tractable A software engineer specialising in realtime data systems. I've worked at companies from Startups to Apple on applications ranging from Ticketing to Genetics. Currently building data systems for training and exploiting Deep Neural Networks.