How to adopt machine learning Slalom

Much has been written about DevOps and its ability to speed up time-to-value and innovation. Machine learning is no different. New approaches and algorithms--for example, deep learning--are coming out all the time, and data scientists are trying them out through code and relying less on GUI-based interfaces. After the new approach has been tested out in a sandbox environment with limited scope, it's time to move toward development, QA, and finally, production. Each one of these environments can be automated with DevOps through tools like Jenkins, Puppet, Chef, Ansible, and Docker.