Optimizing AI Models for Edge Devices Training Course
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
Requirements
- 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
Open Training Courses require 5+ participants.
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