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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

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