GPU & AI Infrastructure Operations
Operate the infrastructure layer under enterprise AI: GPU node pools and scheduling on Kubernetes, serving stacks (vLLM behind LiteLLM), semantic caching, cost-aware start/park automation, and capacity planning for inference — bridging platform engineering and the AI band.
What this course covers.
5 modules, 35 named skill atoms. Expand any module to see them.
1GPU on Kubernetes7 skill atoms
2Serving Stack Assembly7 skill atoms
3Semantic Cache Layer7 skill atoms
4Cost-Aware Automation7 skill atoms
5Inference Capacity Lab7 skill atoms
CL-405 in a role journey.
This course appears in 1 of our 45 role journeys. Here is what a learner takes immediately before and after it in each.
Roles this course serves
This course is authored to band B5.
Every course we run is written to one rung of the CASI ladder, so a plan can be assembled to take a team from where they are to where they need to be.
What do B1–B6 mean?The CASI Capability Ladder — click to expand
Every course targets a band on the CASI Capability Ladder — our six-band proficiency scale, anchored to open standards (O*NET, ESCO, NICE, NIST AI RMF, Bloom's). A band tells you how deep a course goes, and what evidence proves it.
A note on B6. Courses in this catalog target B1–B5. B6 is not taught — it is recognised, through a portfolio and a panel, once someone is setting direction for others. Every journey here is built to land a learner at B5.
Other Cloud courses at this level.
AWS Advanced Networking: Transit Gateway at Enterprise Scale
Azure Virtual WAN & Firewall Networking
Enterprise Multi-Cloud Networking & Connectivity
Cross-Cloud VPN & BGP Engineering Deep-Dive
Global Traffic Management & Cross-Cloud DR
Service Mesh & Advanced Traffic Management
Run CL-405 for your team.
This course runs at several lengths depending on how deep you need to go and how much of it your people already have. Tell us who is being trained and we will scope it.