AI/ML workloads in containers: 6 things to know

Two of today's big IT trends, AI/ML and containers, have become part of the same conversation at many organizations. They're increasingly paired together, as teams look for better ways to manage their Artificial Intelligence and Machine Learning workloads – enabled by a growing menu of commercial and open source technologies for doing so. "The best news for IT leaders is that tooling and processes for running machine learning at scale in containers has improved significantly over the past few years," says Blair Hanley Frank, enterprise technology analyst at ISG. "There is no shortage of available open source tooling, commercial products, and tutorials to help data scientists and IT teams get these systems up and running." Before IT leaders and their teams begin to dig into the nitty-gritty technical aspects of containerizing AI/ML workloads, some principles are worth thinking about up front. Here are six essentials to consider.