Course Outline
Introduction to Edge AI Optimization
- Overview of edge AI and its associated challenges
- The significance of model optimization for edge devices
- Case studies showcasing optimized AI models in edge applications
Model Compression Techniques
- Introduction to the concept of model compression
- Techniques for reducing model size
- Hands-on exercises focused on model compression
Quantization Methods
- Overview of quantization and its advantages
- Types of quantization (post-training, quantization-aware training)
- Hands-on exercises focused on model quantization
Pruning and Other Optimization Techniques
- Introduction to pruning
- Methods for pruning AI models
- Additional optimization techniques (e.g., knowledge distillation)
- Hands-on exercises for model pruning and optimization
Deploying Optimized Models on Edge Devices
- Preparing the environment on the edge device
- 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)
- Utilizing 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
- A foundational understanding of AI and machine learning concepts
- Experience in AI model development
- Basic programming proficiency (Python is recommended)
Target Audience
- AI developers
- Machine learning engineers
- System architects
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.
