Course Code: optaiedge
Duration: 14 hours
Prerequisites:
  • An understanding of AI and machine learning concepts
  • Experience with AI model development
  • Basic programming skills (Python recommended)

Audience

  • AI developers
  • Machine learning engineers
  • System architects
Overview:

Optimizing AI Models for Edge Devices focuses on techniques for optimizing AI models to run efficiently on edge hardware. This course covers model compression, quantization, and other optimization techniques, providing practical knowledge for building performant AI models for edge devices.

This instructor-led, live training (online or onsite) is aimed at intermediate-level AI developers, machine learning engineers, and system architects who wish to optimize AI models for edge deployment.

By the end of this training, participants will be able to:

  • Understand the challenges and requirements of deploying AI models on edge devices.
  • Apply model compression techniques to reduce the size and complexity of AI models.
  • Utilize quantization methods to enhance model efficiency on edge hardware.
  • Implement pruning and other optimization techniques to improve model performance.
  • Deploy optimized AI models on various edge devices.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.
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

Sites Published:

United Arab Emirates - Optimizing AI Models for Edge Devices

Qatar - Optimizing AI Models for Edge Devices

Egypt - Optimizing AI Models for Edge Devices

Saudi Arabia - Optimizing AI Models for Edge Devices

South Africa - Optimizing AI Models for Edge Devices

Brasil - Optimizing AI Models for Edge Devices

Canada - Optimizing AI Models for Edge Devices

中国 - Optimizing AI Models for Edge Devices

香港 - Optimizing AI Models for Edge Devices

澳門 - Optimizing AI Models for Edge Devices

台灣 - Optimizing AI Models for Edge Devices

USA - Optimizing AI Models for Edge Devices

Österreich - Optimizing AI Models for Edge Devices

Schweiz - Optimizing AI Models for Edge Devices

Deutschland - Optimizing AI Models for Edge Devices

Czech Republic - Optimizing AI Models for Edge Devices

Denmark - Optimizing AI Models for Edge Devices

Estonia - Optimizing AI Models for Edge Devices

Finland - Optimizing AI Models for Edge Devices

Greece - Optimizing AI Models for Edge Devices

Magyarország - Optimizing AI Models for Edge Devices

Ireland - Optimizing AI Models for Edge Devices

Luxembourg - Optimizing AI Models for Edge Devices

Latvia - Optimizing AI Models for Edge Devices

España - Optimizing AI Models for Edge Devices

Italia - Optimizing AI Models for Edge Devices

Lithuania - Optimizing AI Models for Edge Devices

Nederland - Optimizing AI Models for Edge Devices

Norway - Optimizing AI Models for Edge Devices

Portugal - Optimizing AI Models for Edge Devices

România - Optimizing AI Models for Edge Devices

Sverige - Optimizing AI Models for Edge Devices

Türkiye - Optimizing AI Models for Edge Devices

Malta - Optimizing AI Models for Edge Devices

Belgique - Optimizing AI Models for Edge Devices

France - Optimizing AI Models for Edge Devices

日本 - Optimizing AI Models for Edge Devices

Australia - Optimizing AI Models for Edge Devices

Malaysia - Optimizing AI Models for Edge Devices

New Zealand - Optimizing AI Models for Edge Devices

Philippines - Optimizing AI Models for Edge Devices

Singapore - Optimizing AI Models for Edge Devices

Thailand - Optimizing AI Models for Edge Devices

Vietnam - Optimizing AI Models for Edge Devices

India - Optimizing AI Models for Edge Devices

Argentina - Optimizing AI Models for Edge Devices

Chile - Optimizing AI Models for Edge Devices

Costa Rica - Optimizing AI Models for Edge Devices

Ecuador - Optimizing AI Models for Edge Devices

Guatemala - Optimizing AI Models for Edge Devices

Colombia - Optimizing AI Models for Edge Devices

México - Optimizing AI Models for Edge Devices

Panama - Optimizing AI Models for Edge Devices

Peru - Optimizing AI Models for Edge Devices

Uruguay - Optimizing AI Models for Edge Devices

Venezuela - Optimizing AI Models for Edge Devices

Polska - Optimizing AI Models for Edge Devices

United Kingdom - Optimizing AI Models for Edge Devices

South Korea - Optimizing AI Models for Edge Devices

Pakistan - Optimizing AI Models for Edge Devices

Sri Lanka - Optimizing AI Models for Edge Devices

Bulgaria - Optimizing AI Models for Edge Devices

Bolivia - Optimizing AI Models for Edge Devices

Indonesia - Optimizing AI Models for Edge Devices

Kazakhstan - Optimizing AI Models for Edge Devices

Moldova - Optimizing AI Models for Edge Devices

Morocco - Optimizing AI Models for Edge Devices

Tunisia - Optimizing AI Models for Edge Devices

Kuwait - Optimizing AI Models for Edge Devices

Oman - Optimizing AI Models for Edge Devices

Slovakia - Optimizing AI Models for Edge Devices

Kenya - Optimizing AI Models for Edge Devices

Nigeria - Optimizing AI Models for Edge Devices

Botswana - Optimizing AI Models for Edge Devices

Slovenia - Optimizing AI Models for Edge Devices

Croatia - Optimizing AI Models for Edge Devices

Serbia - Optimizing AI Models for Edge Devices

Bhutan - Optimizing AI Models for Edge Devices

Nepal - Optimizing AI Models for Edge Devices

Uzbekistan - Optimizing AI Models for Edge Devices