Training
Your engineers learn to answer the question every deployment eventually asks: will it actually run? Not in theory. On hardware, with numbers.
Most AI training teaches people to build models. Almost none teaches them to make models run on the hardware a product actually ships with. We built these programs because that gap is where projects fail, and because it is the work we do every day.
Two Formats. One Standard.
Deploy: Edge AI Engineering
From a trained model to a measured deployment on real hardware.
- Measure what a model actually costs on a target device, correctly
- Identify where a vision pipeline really spends its time
- Take a model from framework to a running deployment on embedded hardware
- Characterize performance under sustained thermal load
- Produce a defensible evidence report for a system's performance
Format: Every participant works at their own NVIDIA Jetson workstation. Two participants per station. All hardware, software, and models provided.
They leave with: A working deployment they built, and a measured evidence report on their own results.
Deploy Short Course
An introduction to edge AI and a live demonstration of what happens when you measure it.
- A working understanding of edge AI and on-device vision, from first principles
- The vocabulary to evaluate a deployment proposal critically
- Live demonstrations on real hardware, measured in the room
- The five questions to ask about any system in their organization
Format: We bring the hardware. Everything demonstrated live. Nothing pre-rendered. Suitable for engineers and technical decision-makers together.
Not a Curriculum. An Evidence Standard.
Participants work on embedded devices, not cloud notebooks. What they measure is what their own products will do.
Every participant produces a measured report on their own work.
These programs come out of our applied research and client engagements, not from a curriculum vendor.
Teams Deploying Vision on Real Hardware
- Engineering teams deploying vision on constrained hardware
- Organizations choosing hardware for a product
- Technical leads who must sign off on feasibility
- R&D groups building on-device AI capability
No prior embedded experience is assumed.
At Our Facility or Yours
Programs run at our facility at NASTP, Rawalpindi, or at your site. Private cohorts for a single organization are available for both formats.
Discuss a cohortTalk to us when you need to know whether it will actually run.
Before you commit to the hardware, the timeline, or the architecture, we can tell you what the numbers are. A 30-minute technical call costs nothing. A measurement baseline gives you the evidence to decide.
We also deliver data analytics & BI and AI automation for selected clients, in partnership with our UK affiliate Femtus Solutions.