Applied AI pathway
Applied AI and ML systems, built against real regional demand.
AI.SPIRE is LevelUp's Applied AI and ML Systems pathway. The current pilot moves learners from AI pre-work into deployed systems: databases, predictive models, NLP, RAG, knowledge graphs, APIs, Docker, monitoring, and capstone demonstrations partners can inspect.
At a glance
A coordinated partner model, not a single vendor.
LevelUp contributes curriculum design, delivery expertise, and systems strategy alongside local operating and funding partners.
- Partners
- LevelUp Economy (curriculum and delivery), Istidama Consulting (local operations), and the Future Skills Fund (funding and oversight).
- Location
- Jordan, delivered hybrid with instructors in the United States and Jordan.
- Current pilot
- A cohort of 51 learners building working AI systems rather than notebooks.
- Status
- In delivery. AI.SPIRE is the first pathway running on the Darb.Tech platform.
Delivery model
How AI.SPIRE moves from training to economic capability.
LevelUp provides curriculum development, program design, and delivery expertise while guiding the wider systems approach around AI capability, entrepreneurship, and employer adoption.
Phase 01
Build the technical operating base
Learners start with Git, Python, SQL, data analysis, testing habits, and reproducible workflows before moving into deeper AI systems work.
Phase 02
Build applied AI system components
Project-based modules move through predictive modeling, NLP, multilingual text handling, RAG, vector retrieval, and knowledge graph enrichment.
Phase 03
Deploy, monitor, and explain the work
Learners integrate FastAPI services, Docker Compose, monitoring, automated evaluation, documentation, and executive communication into portfolio artifacts.
Phase 04
Connect employers and ventures
Employer needs and market opportunities shape capstones that can lead toward jobs, internal innovation, or new company formation.
Portfolio evidence
Every module produces an inspectable artifact.
The pathway is designed so that a partner, an employer, or a funder can look at the work directly instead of taking a completion rate on trust.
- GitHub pull requests reviewed against the same standards a working team would apply
- Labs and integration tasks that build on one another rather than standing alone
- Deployed APIs and reproducible data pipelines
- Automated evaluation routines and written documentation
- An executive-ready capstone briefing partners can sit in on
Formats
The same architecture can stretch or compress.
The pilot is one shape of the pathway. Partners with different timelines, entry points, or institutional constraints can take another.
Current shape
Applied intensive
A project-based pathway for engineers and technical professionals who need working capability on a short timeline.
Extended
Two-year applied pathway
Partners can extend the same architecture into a two-year applied diploma or degree where academic progression and learner support matter more than speed.
Compressed
Workshops and primers
Shorter applied AI and ML workshops for teams where the full intensive is not the right starting point.
Related
Where this sits in the rest of the work.
Curriculum
Applied AI and Machine Learning Systems
The catalog course the AI.SPIRE pathway is built from.
Initiative
Future Skills Fund
The funding and oversight partner supporting the current pilot.
Insight
The AI.SPIRE launch note
Why the pathway was designed this way, and what it is testing.
Capability area
Build talent systems
The pillar AI.SPIRE belongs to: talent, employers, capital, and institutions reinforcing one another instead of running in parallel.
Next step
Run this pathway with us.
Employers can shape capstones and hire from the cohort. Institutions can adapt the architecture. Funders can extend it to another region.