This paper describes various social innovations that can help people around the world embrace open source solutions for social causes. Social innovation from environment and energy conservation to hea …

Josh Patterson, Vartika Singh, David Kale, and Tom Hanlon walk you through interactively developing and training deep neural networks to analyze digital health data using the Cloudera Workbench and Deeplearning4j (DL4J). You'll learn how to use the Workbench to rapidly explore real-world clinical data, build data-preparation pipelines, and launch training of neural networks.

Mercy and Intermountain, two of the largest and most innovative hospital systems in the United States, have recently applied AI to tackle clinical variation within their systems. Todd Steward and Lonny Northrup discuss the application of machine intelligence for optimizing care and provide valuable insights into practice variation for improving clinical pathways.

Ruchir Puri explores the opportunities and challenges of AI for business, focusing on what is needed to truly scale out AI applications and systems across the breadth of enterprises.

AI has the power to transform critical business processes, but new methods will be essential to analyze and visualize datanot as a one-time event but as a continuous process. As a result, a new computing paradigm and deep learning software stack will also be required to power, predict, and act on data to accelerate this transition and produce AI enterprise applications.

This talk will outline how UPMC Enterprises utilizes Kubernetes on-premises and in a public cloud (AWS). Well see how a large enterprise balances SaaS offerings vs Kubernetes hosted services. We will …

More organizations are becoming aware of the value of data and want to get started and scaled up as quickly as possible. But how? Is it possible to get something useful done in five weeks? Kim Nilsson shares her experiences, both good and bad, delivering over 80 five-week data science projects to over 50 organizations, as well as some concrete tips on how to become a data star organization.

Kamran Yousaf explains how to substantially accelerate and radically simplify common practices in machine learning, such as running a trained model in production, to meet real-time expectations, using Redis modules that natively store and execute common models generated by Spark ML and TensorFlow algorithms.

Joseph Blue and Carol Mcdonald walk you through a reference application that processes ECG data encoding using HL7 with a modern anomaly detector, demonstrating how combining visualization and alerting enables healthcare professionals to improve outcomes and reduce costs and sharing lessons learned from their experience dealing with real data in real medical situations.

Life doesnt happen in batches. Being able to work with data from continuous events as data streams is a better fit to the way life happens, but doing so presents some challenges. Ellen Friedman examines the advantages and issues involved in working with streaming data, takes a look at emerging technologies for streaming, and describes best practices for this style of work.