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.
Software AG released it’s top six predictions for Application Integration in 2017. According to David Overos, director of product marketing for integration at Software AG: “Integration is no longer your IT department’s problem; it is everyone’s problem.
This is the first entry in an insideBIGDATA series that explores the intelligent use of big data on an industrial scale. This series, compiled in a complete Guide, also covers the changing data landscape and realizing a scalable data lake, as well as offerings from HPE for big data analytics. The first entry is focused on the recent exponential growth of data.
insideBIGDATA was on hand for the recent Spark Summit East 2017 conference in Boston, and one of the more compelling presentations was by Kavitha Mariappan, VP Marketing at Databricks. The talk focused on the premise that despite the tremendous growth and opportunities in big data today, women still play a small role in this arena.
Given their easy accessibility, many researchers use article abstracts to identify a collection of articles for use in text mining. But, while abstracts provide some valuable pieces of information, there are major advantages to taking steps using and mining full-text articles instead. Read on as Michael Iarrobino, Product Manager at Copyright Clearance Center, explains the advantages of mining full-text articles over abstracts.
The insideBIGDATA Guide to Deep Learning & Artificial Intelligence is a useful new resource directed toward enterprise thought leaders who wish to gain strategic insights into this exciting area of technology. This is the second in a series of articles providing content extracted from the guide. The topic for this segment is the difference between AI, machine learning and deep learning.
In this contributed article, Gladys Kong, CEO of UberMedia discusses how the explosion of mobile data, and specifically mobile location data over the past few years, has brought about an incredible opportunity for businesses.
The insideBIGDATA Guide to Deep Learning & Artificial Intelligence is a useful new resource directed toward enterprise thought leaders who wish to gain strategic insights into this exciting area of technology. In this guide, we take a high-level view of AI and deep learning in terms of how it’s being used and what technological advances have made it possible. We also explain the difference between AI, machine learning and deep learning, and examine the intersection of AI and HPC. We present the results of a recent insideBIGDATA survey, “insideHPC / insideBIGDATA AI/Deep Learning Survey 2016,” to see how well these new technologies are being received. Finally, we take a look at a number of high-profile use case examples showing the effective use of AI in a variety of problem domains.
In this contributed article, Jason Miller, Industrial Applications Engineer at Alpine Data discusses the new PFA standard that takes a good step forward from the previous PMML standard. A shift to PFA has the potential to be a watershed event in predictive analytics.
In this special technology white paper, From Small to Big Data, Adopting the Advanced Analytics Mindset, you’ll learn how to help data teams — analysts, scientists, and managers — to collaborate on data projects. One of the key success factors for these teams is to allow analysts to work on Big Data as easily as they do on smaller data with Excel, as well as to help them find new use cases specific to the data available and the tools at hand.