Course Outline

Introduction to Edge AI for Computer Vision

  • Overview of Edge AI and its benefits
  • Comparison: Cloud AI vs Edge AI
  • Key challenges in real-time image processing

Deploying Deep Learning Models on Edge Devices

  • Introduction to TensorFlow Lite and OpenVINO
  • Optimizing and quantizing models for edge deployment
  • Case study: Running YOLOv8 on an edge device

Hardware Acceleration for Real-Time Inference

  • Overview of edge computing hardware (Jetson, Coral, FPGAs)
  • Leveraging GPU and TPU acceleration
  • Benchmarking and performance evaluation

Real-Time Object Detection and Tracking

  • Implementing object detection with YOLO models
  • Tracking moving objects in real-time
  • Enhancing detection accuracy with sensor fusion

Optimization Techniques for Edge AI

  • Reducing model size with pruning and quantization
  • Techniques for reducing latency and power consumption
  • Edge AI model retraining and fine-tuning

Integrating Edge AI with IoT Systems

  • Deploying AI models on smart cameras and IoT devices
  • Edge AI and real-time decision-making
  • Communication between edge devices and cloud systems

Security and Ethical Considerations in Edge AI

  • Data privacy concerns in edge AI applications
  • Ensuring model security against adversarial attacks
  • Compliance with AI regulations and ethical AI principles

Summary and Next Steps

Requirements

  • Familiarity with computer vision concepts
  • Experience with Python and deep learning frameworks
  • Basic knowledge of edge computing and IoT devices

Audience

  • Computer vision engineers
  • AI developers
  • IoT professionals
 21 Hours

Delivery Options

Private Group Training

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  • Delivered online, onsite/classroom or hybrid by experts sharing real world experience

Private Group Prices RRP from £5700 online delivery, based on a group of 2 delegates, £1800 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.

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