Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
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
14 Hours
Testimonials (2)
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
Course - MLflow
I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.
