Deep learning beyond the learning
Open Source frameworks such as TensorFlow, MXNet, or PyTorch enable anyone to model and train Deep Neural Networks. While there are many great tutorials and talks showing us the best ways for training models, there is few information on what happens after we have trained our model? How can we store, utilize, and update it? In this talk, we look at the complete Deep Learning Pipeline and looks at topics such as deployments, multi-tenancy, jupyter notebooks, model serving, and more.
Jörg Schad is a Developer Evangelist at Mesosphere who works on DC/OS and Apache Mesos. Prior to this he worked on SAP Hana and in the Information Systems Group at Saarland University. His passions are distributed (database) systems, data analytics, and distributed algorithms and his speaking experience include various Meetups, international conferences, and lecture halls.