The presentation below by Alex Smola is “Personalization and Scalable Deep Learning with MXNET” from the MLconf San Francisco, 2016. User return times and movie preferences are inherently time dependent. In this talk, Alex shows how this can be accomplished efficiently using deep learning by employing an LSTM (Long Short Term Model). Moreover, he shows how to train large scale distributed parallel models using MXNet efficiently.
In this contributed article, tech writer Rick Delgado, discusses how the retail world is jumping on the big data analytics bandwagon. Analytics are being used at every stage of the buying process — from predicting popular products to pricing and figuring out what to sell to customers next. Retailers aren’t holding back on what big data can do for them.
In this special guest feature, Pedro Castillo, CEO and Founder of Logtrust, discusses how HBO’s popular Westworld is introducing Big Data and AI concepts to a whole new audience with each episode, and people are noticing. While technologists in Silicon Valley may understand why the Bernard character was able to check legacy data against known (or current) data to find anomalies, the average viewer may not.
In this special guest feature, Rochna Dhand, Director of Product Management at Nimble Storage, argues that a new standard has emerged that organizations must adopt to stay competitive: six-nines availability, or 99.9999 percent up-time. She examines the new standards, the challenges IT teams face from a reactive strategy to availability, and how a predictive approach can help reach a new level of availability.
This is the third article in a series focusing on a technology that is rising in importance to enterprise use of big data – IoT Analytics, or the analytical component of the Internet-of-Things. In this segment, we’ll provide an overview of the rise of IoT analytics. IoT Analytics implies data, fast data, and big data. IoT is not just about capturing sensor data, or GPS locations, or temperature, or velocity changes. You have to find meaning in that data through analytics.
Above the Trend Line: machine learning industry rumor central, is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items such as people movements, funding news, financial results, industry alignments, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz.
In this special guest feature, Julie Lockner, Global Market and Partner Programs, Data Platforms at InterSystems, discusses why businesses don’t need to stop investing in big data, but better manage and analyze the data in their arsenal to provide more personalized customer experiences.
NewVantage Partners, strategic advisors in big data and business innovation to Fortune 1000 businesses, has released the results of its 2017 5th Annual Big Data Executive Survey, entitled “Big Data Business Impact: Achieving Business Results through Innovation and Disruption.” The 2017 Big Data Executive Survey reports what executives from 50 Fortune 1000 firms see as the key factors driving big data adoption, investment – and success.
In this special guest feature, Rajesh Nair, CTO at Tegile Systems, discusses the impact multi-tier flash, NVMe, and scale-out storage technologies will have on the real-time processing of data for IoT, and what we can expect in the near future.
In this contributed article, technology writer and blogger Kayla Matthews takes a high level view of big data by discussing how big data systems can help influence decision-making to reveal important insights. But it’s not always right. The algorithms and systems that big companies are using must be transparent enough that people can challenge decisions that have been made.