04 Artificial Intelligence

Edge AI Implementation

Running AI on your own hardware eliminates cloud dependency, reduces per-query cost, and keeps sensitive business data off third-party infrastructure.

Format 2-day programme or 4 × half-day sessions
Suitable for Developers, IT teams, technical founders
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What you will learn

  • → How to deploy AI systems that run on your own hardware without full dependency on external cloud infrastructure.
  • → How to build multi-agent AI architectures where separate agents handle separate tasks within a single workflow.
  • → How to design fallback and routing logic so AI systems degrade gracefully under load or connection failure.
  • → How to automate business workflows that trigger from real events — form submissions, invoice arrivals, incoming messages — with no human input required.
  • → How to connect locally deployed AI to your existing databases, APIs, and business systems.

Why this matters

The dominant assumption in enterprise AI adoption is that all inference happens in the cloud. For many Malaysian businesses, this creates three compounding problems: recurring cost that scales with usage, latency that makes real-time applications impractical, and data residency concerns that conflict with internal policy or client confidentiality requirements. Edge AI — deploying models on hardware you control — addresses all three. It is not a compromise on capability; modern compact models running locally match or exceed cloud API quality for a wide range of business tasks.

The more significant challenge is not hardware — it is architecture. A single model running locally is straightforward to deploy. A system where multiple specialised agents collaborate, where routing logic sends queries to the right model, where failures degrade gracefully rather than breaking the entire workflow, and where the whole system connects to real business data sources — that requires deliberate design. This programme covers both the deployment fundamentals and the architectural thinking required to build systems that operate reliably in production.

How the training works

The programme is structured across two full days or four half-day sessions, with each block building directly on the previous one. Day one covers the foundation: hardware selection and configuration, model deployment and quantisation trade-offs, and the basic inference pipeline from input to output. Participants deploy a working local AI system on their own hardware by the end of the first day. The session is hands-on throughout — no slides-only sections.

Day two addresses orchestration and integration. Multi-agent architectures are designed and built: how to define agent responsibilities, how to pass context between agents, how to handle failures at each step. The final section covers integration — connecting the deployed system to databases, existing APIs, and real business triggers such as form submissions or incoming messages. Participants leave with a complete, production-ready local AI workflow connected to at least one live data source.

Lead Trainer

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Jayden Sue Jun Hong

Founder, MeetBranding · Kuala Lumpur, Malaysia

Canon EOS Youth Ambassador Alibaba GDT Best Social Impact 2023 MaGICX Technopreneur Grant RM15,000 UTAR BSc (Hons) Software Engineering

Every module taught by this trainer is built from work currently being executed for clients. No guest lecturers. No slide-readers. The strategies covered are the same ones running in active client engagements today.

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