Smart Factory Automation & Edge Computing (TinyML)
Deploy machine learning straight onto microcontrollers. Master IIoT messaging structures, computer vision defect checking, and embedded systems.
Modern plants process data right on the factory floor to bypass cloud latency. Learn to deploy low-latency, optimized classification models to local nodes.
Overview
Programme Overview
What You Learn
Courses & Modules
Highlights
Programme Highlights
overview
Hands-on Microcontroller Integration
Interface code directly with sensory input elements using real-world operational hardware kits.
Real-Time Image Processing
Configure optimized OpenCV structures to manage production metrics and detect component flaws on active paths.
IIoT Protocol Competency
Establish proper communication routing patterns between local mechanical units using industrial messaging paradigms.
Model Footprint Optimization
Learn quantization techniques to shrink complex intelligence frameworks down into compact, efficient setups.
Production Line Transformation
Help industrial workspaces modernize legacy manufacturing sectors with cutting-edge data capture capabilities.
Tools
Gain Exposure to Popular Tools
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Audience Fit
Designed For
A practical route into embedded intelligence, factory automation, and edge analytics.
Electronics, Mechanical & Mechatronics Graduates
Build stronger hardware-software integration skills suited to smart manufacturing roles.
Production & Manufacturing Trainees
Introduce machine vision and local analytics into packaging, inspection, and sorting lines.
Embedded Systems Aspirants
Bridge baseline electronics knowledge with low-latency intelligent automation systems.
Curriculum Focus
Shop-Floor Skill Stack
The path from industrial protocols to edge deployment.
IIoT Architecture & Communication Standards
Use MQTT, OPC UA, and Modbus patterns to move machine diagnostics across industrial systems.
Localized Edge Scripting Engines
Write stable automation logic on compact hardware without depending on constant cloud connectivity.
Computer Vision for Industrial Sorting
Train lean models to detect scratches, missing parts, and physical deviations in real operations.
TinyML Compression & Deployment
Quantize and compress models so they can execute within tight memory and power boundaries.
Outcomes
Deployment Outcome
A portfolio piece built around fast, local decision-making on the factory floor.
Capstone Project
Edge-Based Component Defect Inspection System
Process live camera input on an edge node, flag anomalies in real time, and trigger local alerts without depending on cloud round-trips.
Career Direction
You will learn how to connect industrial messaging, embedded processing, and compact AI models into one usable shop-floor system.
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