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IoT Analytics – Part 5

This is the fifth 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 talk about the challenges of deploying IoT analytics.

Get Up to Speed with Data Science in 7 Easy Steps

In this article, we’ll make sense of data science for those unacquainted with the field and outline a series of 7 easy steps to get up to speed with the technology. In doing so, we’ll highlight the integral steps in the “data science process,” so you can get a good grasp of how data science works and how it is of value to enterprises seeking to maximize the value of their data assets.

IoT Analytics – Part 4

This is the fourth 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 a discussion of IoT analytics value drivers and return-on-investment (RIO) for the enterprise.

Personalization and Scalable Deep Learning with MXNET

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.

How Retailers are Using Big Data to Their Advantage

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.

Westworld’s Big Data Lesson: to Move Forward, We Must Be Able to Look into the Past

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.

Raising the Bar for System Availability

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.

IoT Analytics – Part 3

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” – Your Industry Rumor Central for 1/16/2017

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.

Why Businesses Need to Continue Investing in Data: Big and Small

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.