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
- Strategies for reducing model size
- Practical exercises for model compression
Quantization Methods
- Overview of quantization and its advantages
- Types of quantization (post-training, quantization-aware training)
- Practical exercises for model quantization
Pruning and Other Optimization Techniques
- Introduction to pruning
- Methods for pruning AI models
- Other optimization techniques (e.g., knowledge distillation)
- Practical exercises for model pruning and optimization
Deploying Refined Models on Edge Devices
- Preparing the edge device environment
- Deploying and testing refined models
- Resolving deployment issues
- Practical 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
- Practical exercises with optimization tools
Real-World Applications and Case Studies
- Review of successful edge AI optimization projects
- Discussion of industry-specific use cases
- Practical project for building and optimizing a real-world application
Summary and Next Steps
Requirements
- Familiarity with AI and machine learning concepts
- Experience in AI model development
- Fundamental programming skills (Python is recommended)
Target Audience
- AI developers
- Machine learning engineers
- System architects
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 2600 € + VAT*
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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.
