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Course Outline
Introduction to Edge AI in Computer Vision
- Overview of Edge AI concepts and their operational benefits.
- Comparative analysis: Cloud AI versus Edge AI.
- Critical challenges encountered in real-time image processing.
Deploying Deep Learning Models on Edge Hardware
- Introduction to TensorFlow Lite and OpenVINO frameworks.
- Strategies for optimizing and quantizing models for edge environments.
- Case study: Executing YOLOv8 on an edge device.
Hardware Acceleration for Real-Time Inference
- Overview of edge computing hardware options, including Jetson, Coral, and FPGAs.
- Utilizing GPU and TPU acceleration capabilities.
- Conducting benchmarking and performance evaluation tests.
Real-Time Object Detection and Tracking
- Building object detection systems using YOLO models.
- Implementing real-time tracking for moving objects.
- Improving detection accuracy through sensor fusion techniques.
Optimization Techniques for Edge AI
- Reducing model footprint via pruning and quantization methods.
- Strategies to minimize latency and power consumption.
- Processes for Edge AI model retraining and fine-tuning.
Integrating Edge AI with IoT Systems
- Deploying AI models on smart cameras and various IoT devices.
- The role of Edge AI in facilitating real-time decision-making.
- Managing communication protocols between edge devices and cloud systems.
Security and Ethical Considerations in Edge AI
- Addressing data privacy concerns within Edge AI applications.
- Safeguarding models against adversarial attacks.
- Adhering to AI regulations and ethical AI principles.
Summary and Future Directions
Requirements
- Solid understanding of computer vision fundamentals.
- Proficiency in Python and experience with deep learning frameworks.
- Foundational knowledge of edge computing architectures and IoT devices.
Target Audience
- Computer vision engineers.
- AI developers.
- IoT professionals.
21 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
