01Google Cloud Foundations for AI
12 hoursThe platform underneath the models
- IAM & service accounts
- Cloud Storage & BigQuery as sources
- VPC Service Controls
- GPU & TPU compute options
- Billing, quotas & cost controls
- Cloud Logging & Monitoring
AI
Google AI Cloud Engineer
Structured training in building and deploying AI/ML solutions on Google Cloud — from model foundations through MLOps and production monitoring.
- 50
- hours of live training
- 10 weeks
- at one hour a day, weekdays
- 4
- modules
- 24
- topics covered
Before you start
Some technical background helpful. If you are not sure whether that describes you, the consultation exists to answer exactly that — and to tell you if a different programme is the better start.
Price
- United Kingdom
- £750
- Canada
- $1,750
Tax included — the price shown is the price you pay. This covers the training stage. The bootcamp and placement stages are priced separately.
Curriculum
A module-by-module breakdown
Everything covered in the program, in the order you'll cover it.
02Vertex AI & Gemini
15 hoursBuild on managed foundation models
- Vertex AI Studio & Gemini models
- Grounding & RAG on Vertex
- Vector Search indexes
- Safety filters & responsible AI
- Vertex AI Agent Builder
- Prompt design & evaluation
03Custom Training on Vertex
8 hoursWhen a managed model is not enough
- Vertex AI Workbench
- Custom training jobs & AutoML
- Model Registry & versioning
- Online & batch prediction
- Vertex AI Pipelines
- Feature Store
04AI APIs & Production
15 hoursThe rest of the Google AI surface, in production shape
- Natural Language API
- Document AI for extraction
- Vision AI & Video Intelligence
- Speech-to-Text & Text-to-Speech
- CI/CD with Cloud Build
- Monitoring, drift & a capstone deployment
Projects
Five projects, in Google AI Cloud Engineer
The bootcamp runs the same five stages whatever you enrolled in, so the portfolio you finish with is deep in one technology rather than shallow across several. Each is scoped, designed, built and reviewed.
01 · Foundations
A first build that puts the core concepts to work — environments configured, the basic workflows exercised, the fundamentals proven rather than assumed.
02 · Multiple moving parts
A larger solution with several components and an integration or two, applying the practices that are conventional in your stack rather than improvising.
03 · Architecture that scales
A design-led build: scalability, monitoring and the production-grade patterns your field actually uses, decided before they are implemented.
04 · An enterprise scenario
A realistic situation with services that depend on each other, security to harden and performance to tune — the kind of work that is waiting on the other side of the offer.
05 · Capstone
End to end, everything together: planned, designed, built, deployed and documented well enough to sit at the top of your GitHub profile.
What's included
Everything in the programme
The same seven things in every programme on the site — the subject is what changes.
Live training
Taught live by a practitioner, not pre-recorded video you work through alone.
50 hours
Per programme as standard — 60 for IT Support Analyst, 70 for the AI Consultant and cloud engineering tracks.
Projects
Five briefs in your own stack, each scoped, designed, built and reviewed the way a delivery team would.
Mentoring
One mentor who stays with you for the whole programme, rather than whoever is free that week.
Resume & LinkedIn
Both rewritten around the work you actually built, and positioned for the roles you are targeting.
Interview prep
Mock interviews on the technical and the competency side, including defending the projects you built.
Placement support
Your profile put in front of employers, and the search continues until you are hired.