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Course Code: IT 1885
5 Course Visits
Edge AI for IoT Applications
Course Sector:
Information Technology
Course Dates and Locations
Choose a date and location to book your seat
No.
Date
Days
Location
Fees
Enrollment
01
3 Days
Riyadh, KSA $3,250
02
3 Days
Dubai, UAE $3,250
03
3 Days
Online, Virtual $1,550
Introduction
Training course introducion / brief
As IoT devices become more powerful, there's a growing demand for real-time AI inference directly on edge devices—bypassing the need to send data to centralized cloud systems. Edge AI brings intelligence closer to the data source, enabling faster decisions, improved privacy, reduced latency, and lower bandwidth usage.This course provides a deep dive into designing, optimizing, and deploying machine learning and deep learning models on resource-constrained edge devices. The participants will explore real-world use cases across smart cities, industrial IoT, healthcare, and automotive, focusing on model compression, inference optimization, and energy-efficient design.
Course Objectives
At the end of the training course, participants will be able to
By the end of the course, participants will be able to:
- Understand the principles and challenges of Edge AI in IoT systems.
- Select suitable AI models and edge hardware platforms.
- Optimize and compress models for real-time inference on constrained devices.
- Use tools like TensorFlow Lite, ONNX, OpenVINO, and NVIDIA TensorRT.
- Integrate AI pipelines into IoT architectures with real-time sensors and actuators.
- Measure and optimize for performance, latency, and energy efficiency.
- Design complete Edge AI applications for various domains.
Course Audience
Who is this course for, and can benefit the most
..
Course Outline
The course aims and learning outcomes
Module 1: Introduction to Edge AI and IoT
- What is Edge AI?
- IoT systems overview
- Cloud vs Edge vs Fog computing
- Use cases: smart cities, healthcare, industrial IoT
Module 2: Hardware for Edge AI
- Overview of edge devices (Raspberry Pi, Jetson Nano, Coral, ARM Cortex)
- Hardware constraints: compute, memory, power
- Sensor integration and data acquisition
Module 3: Designing AI Models for the Edge
- Overview of ML/DL models used in IoT
- Choosing the right model architecture
- Lightweight neural networks: MobileNet, SqueezeNet, TinyML
- Real-time requirements and latency constraints
- Quantization (post-training and quantization-aware training)
- Pruning and sparsity
- Knowledge distillation
- Model architecture optimization for embedded systems
Module 5: Deployment Strategies
Module 6: Real-time Decision Making
- AI frameworks for edge deployment (TensorFlow Lite, ONNX, PyTorch Mobile, TVM)
- Model conversion and benchmarking
- End-to-end deployment pipeline
- Real-time inference and streaming data
- Designing feedback loops in edge systems
- Latency and throughput trade-off
Module 7: Energy Efficiency and Power Management
Module 8: Security, Privacy, and Reliability
- Measuring power usage on edge devices
- Energy-efficient model design
- Duty cycling and low-power modes
- Security challenges in Edge AI
- Data privacy at the edge
- Fail-safes and reliability in critical applications
Module 9: Edge AI Applications and Case Studies
- Predictive maintenance in Industry 4.0
- Wearable health monitors
- Traffic monitoring and smart surveillance
- Case Studies
Providers and Associations
Providing the best training services and benefits to our valued clients
Boost certificate of completion
BOOST's Professional Attendance Certificate “BPAC” is always given to the delegates after completing the training course, and depends on their attendance of the program at a rate of no less than 80%, besides their active participation and engagement during the program sessions.
ENDORSED EDUCATION PROVIDER
Flexible deadlines
Customized dates accordance to your schedule
Shareable Certificate
Earn certificate upon completion
COURSE METHODOLOGY
Our Training programs are implemented by combining the participants' academic knowledge and practical practice (30% theoretical / 70% practical activities).
At The end of the training program, Participants are involved in practical workshop to show their skills in applying what they were trained for. A detailed report is submitted to each participant and the training department in the organization on the results of the participant's performance and the return on training. Our programs focus on exercises, case studies, and individual and group presentations.
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