John Mertic and Cupid Chan share real end-user perspectives from companies like GE on how they are using big data tools, challenges they face, and where they are looking to focus investmentsall from a vendor-neutral viewpoint.

The ongoing digitization of the industrial-scale machines that power and enable human activity is itself a major global transformation. Joshua Bloom explains why the real revolutionin efficiencies and in improved and saved liveswill happen when machine learning automation and insights are properly coupled to the complex systems of industrial data.

R has long been criticized for its limitations on scalable data analytics. What's needed is an R-centric paradigm that enables data scientists to elastically harness cloud resources of manifold computing capability for large-scale data analytics. Le Zhang and Graham Williams demonstrate how to operationalize an E2E enterprise-grade pipeline for big data analyticsall within R.

Geospatial analysis can provide deep insights into many datasets. Unfortunately the key tools to unlocking these insightsgeospatial statistics, machine learning, and meaningful cartographyremain inaccessible to nontechnical audiences. Stuart Lynn and Andy Eschbacher explore the design challenges in making these tools accessible and integrated in an intuitive location intelligence platform.

In the realm of predictive maintenance, the event of interest is an equipment failure. In real scenarios, this is usually a rare event. Unless the data collection has been taking place over a long period of time, the data will have very few of these events or, in the worst case, none at all. Danielle Dean and Shaheen Gauher discuss the various ways of building and evaluating models for such data.

The essence of intelligence is the ability to predict. Prediction, perception, planning/reasoning, attention, and memory are the pillars of intelligence. Yann LeCun describes several projects at FAIR and NYU on unsupervised learning, question answering with a new type of memory-augmented network, and various applications for vision and natural language understanding.

The automation of decisions and actions now threatens even knowledge-worker jobs. Tom Davenport describes both the threat of automation and the promise of augmentationcombining smart machines with smart peopleand explores five roles that individuals can adopt to add value to AI, as well as what these roles mean for businesses.

The generalized low-rank model is a new machine-learning approach for reconstructing missing values and identifying important features in heterogeneous data. Through a series of examples, Jo-fai Chow demonstrates how to fit low-rank models in a parallelized framework and how to use these models to make better predictions.

Martin Willcox shares the lessons he's learned from successful Teradata IoT projects about about how to manage and leverage sensor data and explains why data management, data integration, and multigenre analytics are foundational to driving business value from IoT initiatives.

While many companies struggle to adopt big data, a number of industry leaders are leapfrogging big data adoption by going straight to automating core business processes. Andreas Schmidt presents examples from leading European companies that have overcome cultural, technical, and scientific challenges and unlocked the potential of big data in an entirely different way.

Kazunori Sato and Amy Unruh explore how you can use TensorFlow to drive large-scale distributed machine learning against your analytic data sitting in Google BigQuery, with data preprocessing driven by Dataflow (now Apache Beam). Kazunori and Amy dive into practical examples of how these technologies can work together to enable a powerful workflow for distributed machine learning.