AI Stack
A ground-up map of the modern AI engineering stack — models, orchestration, retrieval, and deployment — as one connected system.
One-time course fee
Who should take this?
Developers, data practitioners, technical consultants, and builders moving from prototypes to usable AI applications.
Capability developed
Build, ground, package, deploy, and evaluate AI applications across the modern engineering stack.
Professional application
AI application delivery, technical consulting, product engineering, or forward-deployed work.
What’s inside this track
- 1Model & Prompting Layer
Build: A provider-agnostic prompting layer.
Evidence: Picks the right model for the job instead of defaulting to the familiar one.
- 2Orchestration & Data Layer
Build: A RAG-backed feature wired to a real dataset.
Evidence: Understands exactly how the orchestration and data layers talk to each other.
- 3Serving & Infrastructure
Build: Containerize and serve the Week 2 pipeline behind an API.
Evidence: Moves a notebook prototype into something that survives real traffic.
- 4Full-Stack Integration
Build: Capstone: one full-stack AI feature, model to deployed endpoint.
Evidence: Owns the entire stack end to end, not just one layer of it.
What you get
What you will own
Not a promise — a concrete list of what you personally build and can show a buyer, pulled from this course’s real curriculum.
- A working project: A provider-agnostic prompting layer.
- A working project: A RAG-backed feature wired to a real dataset.
- A working project: Containerize and serve the Week 2 pipeline behind an API.
- A working project: Capstone: one full-stack AI feature, model to deployed endpoint.
- A completed capstone: Adaptive RAG Service with Model-Agnostic Prompting, scored across multiple dimensions
- A portfolio entry documenting how you built it, not just that you finished
- Verified skill evidence — tied to real work, not a self-rating
What you’ll prove — and own — by the end
Completion isn’t the finish line. This is what turns the course into evidence you can actually use.
Personalised from day one
A short entry diagnostic flags which modules to move through quickly and which to slow down on — the full curriculum stays available either way.
Evidence, not checkmarks
Skills track through a real mastery ladder — module completed, exercise practiced, project shipped — not just “lesson viewed.”
Your Final real-world project, AI-defended
Adaptive RAG Service with Model-Agnostic Prompting — submit real work, defend your decisions to an AI interviewer, get scored feedback across multiple dimensions.
A portfolio you can use today
Turn a finished project into a case study, resume bullets, and an interview story — drafted from your real work, yours to review before you use it anywhere.
Connect this course to practical work
See how Ropes links a skill to a business problem, portfolio proof, a bounded service, and responsible client delivery. No client or income outcome is guaranteed.
Earnings potential
A simple illustration of how freelancers commonly structure retainer pricing once they’re taking on client work.
AI feature consulting
Illustrative example — your results will vary based on niche, effort, and client base.
Industry snapshot
Real, sourced research — current tools, market signal, and named case studies, each with a citation. Not marketing copy.