Building a delivery system people can trust
This track is about one thing above all: delivery you can rely on. The DASA DevOps curriculum runs from fundamentals through the Specify-and-Verify professional level, grounded in the DevOps Foundation essentials – collaboration, automation, flow and the cultural change that makes them stick. It is a structured route from “we do some CI” to a team that actually owns its delivery.
On top of that sit the practices that keep a delivery system trustworthy as it scales: continuous integration and delivery done properly, MinimumCD as the floor rather than the aspiration, observability and metrics so you can see what the pipeline is actually doing, and the release discipline that turns deployment from an event into a non-event. This is the bulk of the work and it pays off whether or not a model ever touches your repo.
Where AI changes the picture, it does so by volume: a team shipping faster generates more change for the same pipeline to absorb. So the track adds AI for DevOps practitioners for the automation that genuinely helps, and DevAIOps for treating models as first-class, observable, governed pipeline artefacts rather than scripts someone bolted on.
Get it wrong and the extra speed just turns into incident load; get it right and nobody notices the pipeline at all – which is the point of platform work. The AI-Assisted Software Development path shows the developer side of the same change.