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Duration 14 hours
Course Outline
Foundations of Edge AI
- Core definitions and key theoretical concepts
- Distinguishing Edge AI from Cloud-based AI
- Advantages and potential challenges of edge-centric processing
- Overview of current Edge AI use cases
Edge AI Architecture
- Essential components of an Edge AI system
- Hardware and software prerequisites
- Data flow dynamics within Edge AI applications
- Strategies for integrating with legacy systems
Establishing the Edge AI Environment
- Introduction to leading Edge AI platforms (e.g., Raspberry Pi, NVIDIA Jetson)
- Installation of required software stacks and libraries
- Configuration of the development workspace
- Initial setup and validation of the Edge AI infrastructure
Developing Models for the Edge
- Review of ML and deep learning architectures suitable for edge devices
- Training methodologies tailored for edge deployment constraints
- Optimization techniques to enhance model performance on limited hardware
- Utilization of key frameworks and tools (TensorFlow Lite, OpenVINO, etc.)
Data Management & Preprocessing in Edge Contexts
- Techniques for collecting data in distributed edge environments
- Preprocessing and data augmentation strategies for edge hardware
- Constructing and managing efficient data pipelines on edge nodes
- Safeguarding data privacy and security at the edge
Deploying Edge AI Solutions
- Protocols for deploying models across diverse edge devices
- Monitoring and maintenance techniques for live models
- Executing real-time data inference on edge hardware
- Examination of deployment case studies and practical scenarios
Convergence of Edge AI and IoT
- Linking Edge AI capabilities with IoT sensors and devices
- Selection of appropriate communication protocols and data exchange standards
- Engineering end-to-end solutions combining Edge AI and IoT
- Real-world examples and applied use cases
Practical Applications
- Sector-specific implementations of Edge AI
- Detailed analysis of use cases in healthcare, automotive, and smart home technologies
- Lessons learned from successful industry deployments
- Emerging trends and future opportunities in the Edge AI landscape
Ethics & Best Practices
- Prioritizing privacy and security in edge-based deployments
- Mitigating bias and ensuring fairness in AI models
- Aligning with regulatory frameworks and industry standards
- Adhering to best practices for responsible AI utilization
Practical Projects
- Building a comprehensive Edge AI application
- Engaging with realistic, real-world project scenarios
- Collaborative exercises to foster teamwork and knowledge sharing
- Presentation of projects and constructive peer feedback
Recap & Future Pathways
Requirements
- A solid grasp of fundamental AI and machine learning principles
- Proficiency in programming, with a preference for Python
- Knowledge of edge computing paradigms and IoT frameworks
Target Audience
- Software developers
- IT specialists
Testimonials (1)
That we can cover advance topic and work with real-life example