AI Infrastructure & MLOps for Education & EdTech

A model registry with versioned deployments is what lets you roll back in minutes. Without it, a bad model version becomes a very long evening.

In education & edtech, student data protection and academic integrity constrain what may be automated at all. That single fact reshapes how ai infrastructure & mlops has to be built here, the guardrails, the approval points and the evidence trail are design inputs rather than things bolted on before go-live.

The workload we are most often asked to take on first is content adaptation by level, usually integrated against assessment platforms. We start from the constraint, not the capability, what the system must never do, who signs off, and what happens when it is wrong.

Built by engineers who ship production systems, not by a practice that subcontracts the build. Six weeks to something running in production, not six quarters to a strategy document.