Mirantis Training Now Covers Both Ends of the AI Stack
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Summarize with AI
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GrokMonetize GPU Infrastructure with Mirantis k0rdent AI
15 years of infrastructure expertise, now in a GPU PaaS that makes your AI hardware productive the day it lands.
PUT YOUR GPUS TO WORK
Two new AI courses are available from Mirantis Training. One is about building with AI. The other is about building the infrastructure AI runs on.
AI-DEV 100: Agentic Workflow Workshop takes you from "what is an agent, actually" to a working prototype, chained agents, and an optimized agent that actually gets work done. Four modules, no prerequisites, hands-on labs.
AI-INFRA 100: AI Infrastructure covers the full GPU cluster stack: architecture vocabulary, Redfish and BMC automation, lossless RoCE v2 fabric, and bare-metal provisioning with k0rdent. Four standalone modules, three with labs.
These are two separate tracks, not two rungs of a ladder. Both start at 100 and neither is a prerequisite for the other. The naming reflects that: AI-DEV for building with AI, AI-INFRA for the infrastructure underneath it.
Which one should you take?
Want to build something with AI, or figure out where it fits in your work? AI-DEV 100. No prerequisites, and no expectation that you write production Python every day.
Standing up or operating the hardware AI runs on? AI-INFRA 100, starting with the Foundation module. Assumes you are comfortable with Linux CLI, TCP/IP, server hardware, and BMC/IPMI.
Scoping or advising on AI infrastructure without implementing it? AI-INFRA 100 Foundation gives you the conceptual depth to ask the right questions and stay credible when the stakes are high. Add AI-DEV 100 if agents come up in your conversations.
Genuinely need both? Take them in either order. They do not depend on each other.
Why these two, now
The teams standing up GPU clusters and the teams building agents on top of them are rarely the same people, and they are failing in different ways. Infrastructure engineers with long careers in servers, switches, and VMs are being handed DPUs and Redfish automation with no map. Meanwhile, technical and client-facing staff have built a mental model of AI agents out of demos and vendor decks, which holds up right until someone asks them to evaluate a tool or scope a project.
Both courses address the same underlying problem: the gap between having seen something work and understanding what it was doing. Both close it the same way, by having you do it once in a place where breaking things is fine.
Both come with take-home reference materials and can be scheduled for your team. Get in touch to talk through which one fits.
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