Ray: A Distributed Execution Framework for Emerging AI Applications

James Bradbury offers an overview of PyTorch, a brand-new deep learning framework from developers at Facebook AI Research that's intended to be faster, easier, and more flexible than alternatives like TensorFlow. James makes the case for PyTorch, focusing on the library's advantages for natural language processing and reinforcement learning.

Security will always be very important in the world of big data, but the choices today mostly start with Kerberos. Does that mean setting up security is always going to be painful? What if your company standardizes on other security alternatives? What if you want to have the freedom to decide what security type to support? Yuliya Feldman and Bill ODonnell discuss your options.

In 2016, digital advertising overtook TV in spend, requiring companies to cut through the noise to reach their audience. Manny Puentes explains how Rebel AI decides which ads to serve across devices and how it delivers multidimension reporting in milliseconds.

In pursuit of speed, big data is evolving toward columnar execution. The solid foundation laid by Arrow and Parquet for a shared columnar representation across the ecosystem promises a great future. Julien Le Dem and Jacques Nadeau discuss the future of columnar and the hardware trends it takes advantage of, such as RDMA, SSDs, and nonvolatile memory.

Wee Hyong Tok and Danielle Dean explain how the global, trusted, and hybrid Microsoft platform can enable you to do intelligence at scale, describing real-life applications where big data, the cloud, and AI are making a difference and how this is accelerating the digital transformation for these organizations at a lighting pace.

Seemingly harmless choices in visualization design and content selection can distort your data and lead to false conclusions. Aneesh Karve presents a quantitative framework for identifying and overcoming distortions by applying recent research in algebraic visualization.