Deploy AI where the data lives
A comprehensive guide to running AI models on employee devices, in the browser, and on-premises. Covers WebGPU, quantisation, vLLM, hybrid architectures, and privacy-preserving patterns for regulated industries.
This course is designed for professionals who need to move from AI curiosity to useful implementation. The lessons focus on the workflows, risks, data requirements, governance questions, and ROI arguments that teams need before putting AI into production.
Each module is written as a working guide rather than a theory note. You can read it end to end, share individual lessons with colleagues, or use the module sequence as the outline for an internal workshop.
Explain where AI can help your team, and where it can’t, without overstating what the technology can do.
Identify the data, privacy, workflow, and governance constraints that determine whether an AI use case is ready for production.
Build a clear business case using operational metrics, implementation costs, and measurable outcomes.
Create a practical next-step plan that connects the course material to a pilot, internal training session, or stakeholder discussion.
The tools, guides and services that apply this course to real work.
Use current official guidance for regulated decisions. Individual lessons link further sources where a specific claim depends on them.
Book a 20-minute call about the workflow and your data constraints. We’ll tell you whether on-device or on-premises inference fits, and what a first version would take.