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Course Outline
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Introduction to Edge AI
- Defining Edge AI and its strategic significance
- Advantages of deploying AI models at the edge
- Overview of the AI landscape in edge computing
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Convolutional Neural Networks (CNN) Architectures for Edge AI
- Understanding CNN fundamentals and their relevance to Edge AI
- Design considerations for CNNs on resource-constrained devices
- Case studies: Efficient CNN models in practice
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Designing Compact Networks for Edge Deployment
- Techniques for minimizing model size while preserving accuracy
- Tools and frameworks for model optimization
- Evaluating the balance between performance and complexity
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Knowledge Distillation Techniques for Edge AI
- Core principles of knowledge distillation and its advantages
- Applying knowledge distillation to edge-based models
- Practical examples and success stories
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Deep Compression Methods for Edge AI Models
- Overview of model compression techniques (pruning, quantization)
- Application of compression methods in edge AI contexts
- Impact on performance, accuracy, and deployment strategies
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Federated Learning Concepts and Applications
- Introduction to federated learning and its role in privacy and efficiency
- Architectural and operational aspects of federated learning systems
- Addressing challenges and solutions for federated learning at the edge
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Implementing Edge AI Solutions
- End-to-end workflow for deploying AI models on edge devices
- Tools and platforms supporting Edge AI development
- Monitoring and managing Edge AI applications in production
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Case Studies and Project Work
- Analyzing real-world Edge AI deployments across various industries
- Group project: Design and implementation of an Edge AI solution
- Presentation and review of project outcomes
Requirements
- Familiarity with cloud computing and artificial intelligence
Audience
- Business Analysts
- Product managers
- Developers
35 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
