For the longest time data science was often performed in silos, using large machines with copies of production data. This process was not easily repeatable, explainable or scalable and often introduced business and security risk. With modern enterprises now adopting a DevOps engineering culture across their applications stack, no longer can machine learning development practises operate in isolation from the rest of the development teams.
Thankfully – earlier this year Microsoft GA’d a new service called Azure Machine Learning Services which provides data scientists and DevOps engineers a central place in Azure to create order out of what can be a complicated process.
This blog post outlines the DevOps process when applied to ML. I have also presented on this topic several times, see My Presentation section here – Azure ML DevOps Workflow (wordpress.com)Continue reading