Machine learning projects face data prep, model building hurdles

Machine learning has been part of the advanced analytics picture for decades, but the emergence of big data platforms and better tools for creating automated analytical algorithms is bringing it more front and center. As a result, growing numbers of IT and analytics teams face the challenges of making machine learning projects work. In many organizations, machine learning initiatives require big investments in IT infrastructure, often involving the deployment of Hadoop clusters, the Spark processing engine and other big data technologies. New data management and analytics processes are often also needed to get data sets ready for analysis and to develop the algorithms that will be run against them. In many cases, that means adding new skills through outside hiring or retraining of existing employees.