GCP AI
Building and deploying on Google Cloud's AI stack — Vertex AI, Gemini, and production ML pipelines.
One-time course fee
Who should take this?
Cloud, platform, data, and application professionals building within Azure, AWS, or Google Cloud environments.
Capability developed
Select, configure, secure, and deliver managed AI services and production pipelines on a major cloud platform.
Professional application
Cloud AI implementation, platform delivery, certification-backed technical work, or enterprise integration.
What’s inside this track
- 1Vertex AI Platform Overview
Build: Train a model both ways — AutoML and custom — on the same dataset.
Evidence: Knows which Vertex AI path fits a given problem.
- 2Building with Gemini
Build: A Gemini-powered feature via Vertex AI Studio.
Evidence: Comfortable building real generative features on GCP, not just calling an API in isolation.
- 3Production Pipelines
Build: An end-to-end Vertex AI Pipeline for a real prediction task.
Evidence: Has a reproducible, production-style pipeline on GCP.
- 4RAG & Deployment
Build: Capstone: a RAG-powered search app on your own data, deployed on GCP.
Evidence: Ships a real GCP-hosted GenAI product end to end.
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: Train a model both ways — AutoML and custom — on the same dataset.
- A working project: A Gemini-powered feature via Vertex AI Studio.
- A working project: An end-to-end Vertex AI Pipeline for a real prediction task.
- A working project: Capstone: a RAG-powered search app on your own data, deployed on GCP.
- A completed capstone: Enterprise RAG Search Service on Vertex AI, 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
Enterprise RAG Search Service on Vertex AI — 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.
Cloud AI implementation
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.