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Course Outline
- Introduction to Edge AI
- Defining Edge AI and its significance
- Benefits of deploying AI models at the edge
- Overview of the AI landscape for edge computing
- Convolutional Neural Networks (CNN) Architectures for Edge AI
- Understanding CNN basics and their applicability to Edge AI
- Design considerations for CNNs on edge devices
- Case studies: Efficient CNN models in action
- Designing Compact Networks for Edge Deployment
- Techniques for reducing model size without sacrificing accuracy
- Tools and frameworks for model optimization
- Evaluating trade-offs between performance and complexity
- Techniques in Knowledge Distillation for Edge AI
- Principles of knowledge distillation and its benefits
- Implementing knowledge distillation for edge models
- Practical examples and success stories
- Deep Compression Methods for Edge AI Models
- Overview of model compression techniques (pruning, quantization)
- Application of compression methods to edge AI scenarios
- Impact on performance, accuracy, and model deployment
- Federated Learning Concepts and Applications
- Introduction to federated learning and its importance for privacy and efficiency
- Architectural and operational aspects of federated learning systems
- Challenges and solutions in implementing federated learning at the edge
- 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
- Case Studies and Project Work
- Analyzing real-world Edge AI deployments across various sectors
- Group project: Design and implement an Edge AI solution
- Presentation and critique of project outcomes
Requirements
- Familiarity with cloud computing and artifical intelligence
Audience
- Business Analysts
- Product managers
- Developers
35 Hours
