Clear the path to continuous intelligence with machine learning, consultancy urges ZDNet

What do technology leaders and professionals need to do to help their organizations achieve the holy grail of continuous intelligence? Look to artificial intelligence and machine learning to pave the way. However, achieving a state of continuous intelligence isn't an overnight sprint by any means -- many organizations aren't quite ready to bring together the adroit data management, automation, processes and skills needed to make things happen. That's the word from a three-part series published by ThoughtWorks, which advocates an approach it calls Continuous Delivery for Machine Learning (CD4ML), "a software engineering approach in which a cross-functional team produces machine learning applications based on code, data, and models in small and safe increments that can be reproduced and reliably released at any time, in short adaptation cycles." Employing data "to produce tangible outcomes for business is the real value driver and for that, we are seeing the world moving more towards intelligence," write Ken Collier, Mark Brand and Pramod N, all with ThoughtWorks.