AI Project Building Course — Cohort 9
An in-person, applied AI course that takes participants from a problem to a working prototype using CRISP-DM.
Context
Cohort 9 of the AI Project Building Course was a three-day, in-person programme that NLAI delivered in a training partnership with EMC (Educational Mastar Central). International Al-Ghurmani Company covered 50% of each participant's fee. The course project was a solar-panel inspection prototype that runs in the browser.
The course is designed for professionals, entrepreneurs, students and teams who want to turn an AI idea into a practical project. No prior experience in AI, programming or data analysis is required.
The ninth cohort met in person in Oman over three days in July 2026. It was a paid course; EMC (Educational Mastar Central), a Dutch training institute, is NLAI's training partner for the programme.
Challenge
Most AI courses stop at theory or at tool demos. Professionals, entrepreneurs and students who want to use AI in their work need to experience the whole path: choosing a problem worth solving, understanding the data it needs, building something that works and judging honestly whether it is good enough. The course had to make that path achievable in three days, including for people who had never written code.
NLAI role and contribution
NLAI designed and delivered the course, and ran its operations:
- Curriculum — a three-day path from problem definition to a presented prototype, built on CRISP-DM.
- Teaching — hands-on sessions that draw on the trainer's experience of previous AI projects, including the mistakes that usually cost teams time.
- Course project — a working solar-panel inspection prototype, used to show every stage in practice.
- Operations — online enrolment with a clear fee and consent step, and short feedback forms after each day.
Approach
The course follows CRISP-DM, a widely used method for data and AI projects, through six stages:
- Understand the problem and the project objective.
- Understand the data the project needs.
- Collect and prepare the data.
- Build the model or intelligent solution.
- Test and evaluate the results.
- Present the project and prepare it for use and further development.
Concepts are explained simply and applied immediately, and AI tools are used to build the project, so participants without a programming background can complete each stage.
Architecture
Input
- Photos of solar panels
- Live camera stream
Detection model
- Compact object-detection modelYOLO
- Exported for the browserONNX
In-browser inference
- Runs on the device's GPU, or on its CPU as a fallbackWebGPU · WebAssembly
- Images are analysed on the device
Inspection view
- Dust, bird droppings, snow and damage marked on the image
- Counts of affected panels and damage points
Implementation
01
Day 1 — From problem to project
Choose the problem and the target user. Define value, scope and success measures.
02
Day 2 — Prototype and validate
Build the core AI workflow. Test assumptions and improve the experience.
03
Day 3 — Launch roadmap
Plan data, operations, safety and measurement. Present the project and define next actions.
Outcome
- Delivered in person over three days in July 2026.
- A working solar-panel inspection prototype built as the course project, running entirely in the browser.
- Participant feedback collected after each day through NLAI's feedback forms.
Verified figures
- Enrolment applications recorded
- 27
- Source: the programme's enrolment records, verified 18 September 2026
The figure counts every enrolment application recorded for the cohort, whatever its status: reserved, paid, cancelled or on the waiting list.
Technologies and methods
Technologies
- YOLO
- ONNX
- ONNX Runtime Web
- WebGPU
- WebAssembly
Methods
- CRISP-DM project method
- Object detection
- In-browser inference
- AI-assisted building for non-programmers
What's next
Future cohorts are announced on the NLAI training pages. Organisations can also commission the same applied format for their own teams, built around a problem from their own operations.
Partners and relationship
Each partner is listed with its relationship to this work. None of them is presented as an NLAI client.
- Educational Mastar Central (EMC)Training partner
- International Al-Ghurmani CompanyCo-funder
Where this connects
The practice and capabilities this work draws on, for readers who want the technical depth.
- Practice
- Capability Building
- Related pages
Capability building
Planning applied AI training for your team?
We can shape the same problem-to-prototype format around a challenge from your own operations. Start with a short consultation about goals, audience and timing.