DevOps and platform engineering teams are under constant strain. Output expectations remain high, headcount is constrained, and compliance requirements are not optional.
Much of the work is repetitive but complex: translating requirements into configuration, delivery controls, processes, and operational responses. This is exactly the kind of work where AI can help — provided it operates with context, guardrails, and clear limits.
Most teams are still using AI for isolated generative tasks. The bigger opportunity is using it to support how platforms are built, operated, and improved.
Where AI creates real leverage
This workshop focuses on the practical use of AI in platform and service environments, where reliability, traceability, and secure defaults matter.
You will learn how to use AI to:
produce spec-driven configuration faster and more consistently
reduce operational load with stronger incident triage and first response
create an intelligent support layer that evolves with the platform
improve standardisation without forcing teams into rigid process change
This is not about handing control to autonomous systems. It is about giving teams structured, measurable leverage through specifications, processes, and tools that can validate both human input and AI output.
Tool-agnostic by design
We teach the operating model and AI capability patterns first, then show you how to adapt them to your stack.
Whether you run Kubernetes or serverless workloads, and whether your teams use AWS, Azure, or Google Cloud, the focus is the same: practical AI capabilities that reduce toil, improve support, and fit inside existing engineering controls.
Who this is for
This course is designed for:
platform engineers
DevOps leaders
SRE and operations teams
engineering managers responsible for delivery controls
organisations adopting AI under compliance or governance constraints
Best fit for teams that:
operate production platforms under security or compliance requirements
want to reduce operational toil without weakening control
need practical AI patterns that fit existing workflows and approval models
