Machine learning gets boost from growing big data ecosystem

New parallel processing platforms in the growing big data ecosystem are enabling organizations to bring greater compute power to bear on analytical problems. And machine learning applications are likely to be among the leading uses for systems based on big data technologies such as Hadoop and Spark. They want to build complex machine learning models and run the models repeatedly to fine-tune the algorithms and improve the results -- and they want to do that work as quickly as possible so they can handle a greater number of analytical problems, according to presentations and discussions at the Strata Hadoop World 2015 conference held recently in San Jose, Calif. In this Talking Data podcast, SearchDataManagement's Jack Vaughan, who covered the conference, tells colleague Ed Burns that people are coming to machine-learning applications from a couple different points of view. One group includes data analysts and programmers at e-commerce websites who want to serve up recommendations to visitors. Another includes enterprise statisticians who have been immersed in the technology for years but haven't had the processing power needed to move beyond relatively simple models.