"This course opened my eyes to what’s possible with AI on edge devices. The practical exercises on optimisation and deployment were fantastic, and I now feel confident building AI solutions for IoT and autonomous systems. Highly recommended!"
This course teaches learners how to develop AI solutions for edge devices and embedded systems, where low latency, efficiency, and real-time processing are critical. Participants explore model optimisation, quantisation, hardware constraints, and deployment to microcontrollers, GPUs, and edge servers. The course combines theory and hands-on projects using Python, TensorFlow Lite, and other frameworks, enabling learners to build AI systems for robotics, IoT, drones, autonomous vehicles, and smart devices. By the end, participants will be able to design and deploy AI models that function efficiently on constrained hardware while maintaining performance.
Module 1: Edge AI Fundamentals & Hardware Constraints
Learners begin by understanding the challenges and opportunities of running AI on edge devices. This module covers low-power computing, embedded system architectures, memory and processing limitations, and performance trade-offs. Participants learn how to design AI pipelines suited for real-world edge applications.
Module 2: Model Optimisation & Quantisation
This module focuses on adapting AI models for edge deployment. Topics include pruning, quantisation, knowledge distillation, and lightweight model design. Learners implement practical techniques to reduce model size and computational load while maintaining accuracy, ensuring AI models run efficiently on constrained hardware.
Module 3: Deployment & Real-Time Edge Applications
Learners gain hands-on experience deploying AI models to edge devices and embedded systems. The module covers microcontrollers, embedded GPUs, IoT devices, and real-time inference. Participants also learn best practices for monitoring, updating, and maintaining AI applications in production environments.
This course is ideal for AI developers, software engineers, robotics specialists, and technical professionals interested in building AI systems for constrained hardware. It is also suitable for students and career changers looking to specialise in edge AI and embedded intelligence.
We guide organisations in adopting AI to automate tasks, streamline processes, and free up valuable time so your team can focus on what really matters.
Our hands-on training helps staff build confidence in using AI tools, from automation to data analysis, improving both efficiency and decision-making.
We design and support AI systems tailored to your organisation, ensuring smooth integration with existing workflows and long-term efficiency gains.
Attending this course equips learners with the skills to develop, optimise, and deploy AI systems on edge devices. The hands-on approach ensures participants understand hardware constraints, performance optimisation, and real-time deployment, enabling them to build AI solutions for autonomous systems, IoT, and smart devices.
Our inhouse programmes are easy to arrange and can be tailored to meet more specific learning needs. Training can be run from your own premises, or we will help you select a suitable venue offsite. Our existing IT courses can be customised to meet your organisation requirements. With all our courses there are no hidden cost, the price you see is the price you pay.
With Infinity AI's inhouse training, your team can gain the essential IT skills they need without the inconvenience of off-site sessions. Our expert-led courses are designed to fit your business, maximising participation and providing customised solutions that boost productivity and performance. Let us bring the training to you, ensuring your entire team is equipped for success.
Inhouse Training
Infinity AI Learning provides expert-led, practical AI training focused on real-world applications. Our Edge AI course combines hands-on projects, mentorship, and exposure to industry-standard tools, preparing learners to build efficient, production-ready AI systems for high-growth sectors such as robotics, IoT, and autonomous systems.
