This is a visitor publish by Jimmy Whitaker, senior knowledge science evangelist at Pachyderm
In the previous few years, DevOps has begun to shift—from a tradition of steady integration and steady deployment (CI/CD) greatest practices to leveraging Git-based methods to handle software program deployments. This transition has made software program much less error susceptible, extra scalable, and has elevated collaboration by making DevOps extra developer-centric.
At its coronary heart, this simplification lets builders use the identical highly effective model management system they’re used to with Git as a method to ship infrastructure as code, documentation, and even Kubernetes configurations. It works by utilizing Git as a single supply of fact, letting you construct strong declarative apps and infrastructure with ease. But in the case of machine studying functions, DevOps will get rather more complicated.