Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 35 hours
Course Outline
-
Introduction to Edge AI
- Defining Edge AI and its importance
- Advantages of deploying AI models at the edge
- Survey of the AI landscape in edge computing
-
Convolutional Neural Networks (CNN) Architectures for Edge AI
- Foundations of CNNs and their relevance to Edge AI
- Design factors for CNNs on edge devices
- Case studies: Practical applications of efficient CNN models
-
Designing Compact Networks for Edge Deployment
- Methods to reduce model size while maintaining accuracy
- Tools and frameworks for optimizing models
- Assessing the balance between performance and complexity
-
Knowledge Distillation Techniques for Edge AI
- Core principles of knowledge distillation and its advantages
- Applying knowledge distillation to edge models
- Real-world examples and success stories
-
Deep Compression Methods for Edge AI Models
- Overview of model compression techniques such as pruning and quantization
- Applying compression methods to edge AI contexts
- Effects on performance, accuracy, and deployment
-
Federated Learning: Concepts and Uses
- Introduction to federated learning and its role in privacy and efficiency
- System architecture and operational details of federated learning
- Addressing challenges and solutions in edge federated learning
-
Building Edge AI Solutions
- Complete workflow for deploying AI models on edge devices
- Platforms and tools for Edge AI development
- Monitoring and administration of Edge AI apps in production
-
Case Studies and Projects
- Reviewing real-world Edge AI implementations in different industries
- Team project: Develop and implement an Edge AI solution
- Presenting and evaluating project results
Requirements
- Basic understanding of cloud computing and artificial intelligence
Target Audience
- Business Analysts
- Product Managers
- Developers
Testimonials (1)
That we can cover advance topic and work with real-life example
