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Course Outline

Introduction to Edge AI

  • Core definitions and concepts
  • Distinguishing Edge AI from cloud-based AI
  • Key benefits and common use cases
  • Survey of available edge devices and platforms

Configuring the Edge Environment

  • Overview of edge hardware (e.g., Raspberry Pi, NVIDIA Jetson)
  • Installation of required software and libraries
  • Setup of the development environment
  • Hardware preparation for AI integration

Building AI Models for the Edge

  • Overview of machine learning and deep learning models suitable for edge devices
  • Methods for training models in local and cloud settings
  • Optimization techniques for edge deployment (such as quantization and pruning)
  • Key tools and frameworks for Edge AI development (TensorFlow Lite, OpenVINO, etc.)

Deploying AI Models on Edge Hardware

  • Process for deploying AI models across different edge devices
  • Handling real-time data processing and inference on the edge
  • Oversight and management of deployed models
  • Illustrative examples and case studies

Practical AI Applications and Projects

  • Creating AI applications for edge devices (e.g., computer vision, NLP)
  • Project: Constructing a smart camera system
  • Project: Integrating voice recognition on edge hardware
  • Group collaboration on real-world scenarios

Performance Assessment and Refinement

  • Methods for assessing model performance on edge devices
  • Utilizing tools for monitoring and debugging Edge AI apps
  • Tactics for optimizing AI model efficiency
  • Mitigating challenges related to latency and power usage

Integration with IoT Systems

  • Connecting Edge AI solutions with IoT devices and sensors
  • Understanding communication protocols and data exchange methods
  • Constructing an end-to-end Edge AI and IoT solution
  • Practical integration demonstrations

Ethical and Security Perspectives

  • Safeguarding data privacy and security in Edge AI applications
  • Mitigating bias and ensuring fairness in AI models
  • Adhering to relevant regulations and standards
  • Best practices for responsible AI deployment

Hands-On Projects and Exercises

  • Developing a comprehensive Edge AI application
  • Working on real-world projects and scenarios
  • Participating in collaborative group exercises
  • Presenting projects and receiving feedback

Requirements

  • A solid grasp of AI and machine learning fundamentals
  • Proficiency in programming languages (Python is preferred)
  • Knowledge of edge computing principles

Target Audience

  • Software Developers
  • Data Scientists
  • Tech Enthusiasts
 14 Hours

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  • Format: Online (live), In-company (at your offices), or Hybrid.
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