Course Outline

Introduction to Edge AI Optimization

  • Overview of edge AI and its challenges
  • Importance of model optimization for edge devices
  • Case studies of optimized AI models in edge applications

Model Compression Techniques

  • Introduction to model compression
  • Techniques for reducing model size
  • Hands-on exercises for model compression

Quantization Methods

  • Overview of quantization and its benefits
  • Types of quantization (post-training, quantization-aware training)
  • Hands-on exercises for model quantization

Pruning and Other Optimization Techniques

  • Introduction to pruning
  • Methods for pruning AI models
  • Other optimization techniques (e.g., knowledge distillation)
  • Hands-on exercises for model pruning and optimization

Deploying Optimized Models on Edge Devices

  • Preparing the edge device environment
  • Deploying and testing optimized models
  • Troubleshooting deployment issues
  • Hands-on exercises for model deployment

Tools and Frameworks for Optimization

  • Overview of tools and frameworks (e.g., TensorFlow Lite, ONNX)
  • Using TensorFlow Lite for model optimization
  • Hands-on exercises with optimization tools

Real-World Applications and Case Studies

  • Review of successful edge AI optimization projects
  • Discussion of industry-specific use cases
  • Hands-on project for building and optimizing a real-world application

Summary and Next Steps


  • An understanding of AI and machine learning concepts
  • Experience with AI model development
  • Basic programming skills (Python recommended)


  • AI developers
  • Machine learning engineers
  • System architects
 14 Hours

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