The open curriculum for AI infrastructure careers.
A free, open-source ladder of role-based programs — from your first production ML service to enterprise architecture, with sibling curricula for ML engineering, AI engineering, and governance. The whole curriculum builds in the open on GitHub. The first live cohort is enrolling now.
Join the first cohort →Junior AI Infrastructure Engineer
16 weekly live sessions, one production-shaped ML service shipped to your GitHub by Demo Day. Founding Cohort $199. The on-ramp to the whole ladder.
One ladder, learnable two ways.
Most courses teach a tool. This is a career path — a coherent ladder of AI-infrastructure roles, each mapped to what employers actually hire for. Learn any of it free on GitHub, or join a live cohort when one opens for your rung.
Free and open-source
Every track's lessons, exercises, and reference solutions live in the open on GitHub. No paywall on the content — ever. The free curriculum is the funnel, not the product.
A role-based ladder
Eleven tracks from Junior Engineer to Principal Architect, plus MLOps, platform, and performance — with sibling curricula for ML engineering, AI engineering, and governance so you can see the whole path, not just the next step.
Built from real hiring data
Each track is shaped by actual job postings for that role and kept current as the market moves — so what you learn matches what hiring managers ask about.
Eleven roles. One coherent path.
The Junior track is live as a cohort and ready to learn. The rest of the ladder is under active development, building in the open — open any role to follow its progress and join the waitlist.
Engineering ladder
Junior AI Infrastructure Engineer
The on-ramp: ship one production-shaped ML service over 16 weeks — containers, Kubernetes, CI/CD, monitoring, and a real capstone.
AI Infrastructure Engineer
The core production role: containerize, deploy, and operate ML services on Kubernetes with CI/CD, monitoring, and cost control. The deepest, most hands-on track in the ladder.
Senior AI Infrastructure Engineer
Run ML infrastructure at scale: SLOs, on-call, capacity planning, multi-tenant platforms, and the judgment calls that keep production healthy under load.
Principal AI Infrastructure Engineer
Set technical direction across teams: reference architectures, platform strategy, and the hard trade-offs that shape how an org builds and runs AI infrastructure.
Architecture ladder
AI Infrastructure Architect
Design the systems: end-to-end ML platform architecture, data and serving layers, reliability and cost as first-class constraints, and the diagrams that align teams.
Senior AI Infrastructure Architect
Architect across the enterprise: multi-region, multi-tenant platforms, governance, and the long-horizon decisions that outlive any single project.
Principal AI Infrastructure Architect
Own the architectural vision org-wide: standards, paved roads, and the strategy that turns scattered ML work into a coherent platform.
Platform & specialties
MLOps Engineer
Own the ML lifecycle in production: pipelines, experiment tracking, model registries, CI/CD for models, monitoring, and progressive rollout.
ML Platform Engineer
Build the internal platform other ML teams build on: paved roads, self-service, feature and serving infra, GPU scheduling, and cost levers.
AI Performance Engineer
Make ML systems fast and cheap: profiling, quantization and compression, batching, CUDA-level optimization, and serving economics.
The curriculum family
This site covers AI infrastructure — running the platforms. Three sibling curricula cover the rest of the AI landscape, organized by what you do with a model:
Bringing a whole team?
Private cohorts mapped to your stack and your career ladder. The curriculum stays free and open — companies license the delivery.
The instructor
Joshua Ferguson
Senior Systems Engineer · Phoenix, AZ. 15+ years building production infrastructure across enterprise cloud platforms, public sector, and AI-adjacent tooling.
I've spent the last decade and a half wiring up the infrastructure that real businesses run on — multi-datacenter automation, container orchestration, AWS at enterprise scale, the CI/CD that lets a team ship without paging someone every Friday night. This isn't material I read about and turned into slides. It's material I've shipped, broken, fixed, and shipped again.
The reason I'm teaching AI infrastructure now is that I've been living the transition from traditional infrastructure to AI-adjacent platform work for the last two years — including building APEX, a multi-agent orchestration platform on Anthropic's Claude Agent SDK. The curriculum is shaped by both halves of that work: the fundamentals that don't go away, and the ML-specific gotchas that traditional infra training misses.