To help our audience leverage the power of machine learning, the editors of insideBIGDATA have created this weekly article series called “The insideBIGDATA Guide to Machine Learning.” This is our seventh installment, “Production Deployment with R.”
Teradata (NYSE: TDC), the analytic data platforms, marketing applications, and services company, today introduced Teradata® Aster® R, which extends the power of open source R analytics by lifting the memory and processing limitations. Teradata Aster R offers the R analyst an enterprise-ready business analytics solution that is massively scalable, reliable, and easy-to-use.
I am really looking forward to attending my first useR! conference – coming to Los Angeles June 30-July 3. I’ll be at the big show to cover all the late breaking R news and report back to you all here. This annual conference jumps around the globe, alternating between the U.S. and Europe (last year’s conference was held in Spain). This year the conference will take place on the campus of the University of California at Los Angeles (UCLA).
It was Saturday, June 14 and I was up at the crack of dawn (which is quite an achievement for a late night data hacker like me) to get over to the Big Data Camp 2014 happening at the DirectTV campus in beautiful El Segundo, Calif. (actually a spartan industrial area just south of LAX). I was anticipating a great day of big data technology focusing on three advertised session tracks: Data Science, Hadoop, and NoSQL.
I love a good data science competition to let me stretch my arms around a good problem. Kaggle is one of my favorite destinations these days to learn about all the innovative ways machine learning is being applied to real-life business problems. So I was pleasantly surprised to see this new challenge sponsored by Algomost, an international data mining platform.
Seventy-five percent of businesses have yet to successfully deploy big data analytics solutions to gain business-impacting insights, despite 65 percent increasing their investment in analytic services and technologies in 2014. These findings are part of “Analytics 2014,” Lavastorm’s second annual survey on analytic usage, trends, and future initiatives.
Everyone knows that data scientists love data and the more of it, the greater the love. As a result, the surging interest in wearables is just what the doctor ordered because these electronic devices collect enormous treasure troves of data. In turn, it is the job of data scientists to make sense of it all, unlock secrets, and assign economic value. As a data scientist, it is a dream come true!
I found an interesting discussion going on in the Global Big Data & Analytics group on LinkedIn – “Why do Hadoop projects fail?” Having just returned from the Hadoop Summit 2014 in San Jose, I witnessed plenty of use case examples Hadoop implementations that were wildly successful. I was therefore intrigued by the notion to itemize causes for failed projects.