DevOps for AI Apps (Part 2)

Welcome back to the second of this two-part piece on DevOps for AI Apps. In part one, we explored the differences between regular apps and AI apps, drilled into some of the added complexity and thought about the benefits of adopting a DevOps methodology for enterprise AI app projects. In this part, let’s take a look at some further justification for DevOps, the importance of automation, and suggest places to start and how VMware can help.

Continual Iteration

A core concept of DevOps is that it is continual. You set up the pipe and keep sending things down it, from the developer’s laptop, into production. Looking back at the DevOps approach for a traditional app, or even a container based/micro-services app, we know that it’s really the app itself that’s important (and things like the DBs it might connect to). Using DevOps can be a great way to iterate through the development lifecycle quickly, so that when there is a patch required, or a new feature needs to be released, the important code is updated and integrated, the change gets automatically propagated through the test environment and finally, it’s released into production very quickly.

This process changes in the AI App scenario. In this scenario, we have more than just the code to worry about. We have three equally important pillars, continually changing, for potentially different reasons..