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Duration 21 hours
Course Outline
Core Principles of TinyML Pipelines
- Summary of the TinyML process stages
- Attributes of edge hardware
- Key considerations for pipeline architecture
Data Acquisition and Preparation
- Gathering structured and sensor data
- Strategies for labelling and augmenting data
- Preparing datasets for resource-limited environments
TinyML Model Construction
- Choosing model architectures suited to microcontrollers
- Training procedures using standard ML frameworks
- Assessing model performance metrics
Model Refinement and Compression
- Quantisation methods
- Pruning and weight sharing techniques
- Optimising the balance between accuracy and resource limitations
Model Transformation and Packaging
- Exporting models to TensorFlow Lite
- Incorporating models into embedded toolchains
- Managing model size and memory restrictions
Implementation on Microcontrollers
- Installing models onto hardware targets
- Setting up run-time environments
- Testing real-time inference
Monitoring, Testing, and Validation
- Testing approaches for deployed TinyML systems
- Troubleshooting model behaviour on hardware
- Validating performance under field conditions
Integrating the Complete End-to-End Pipeline
- Creating automated workflows
- Version control for data, models, and firmware
- Managing updates and iterations
Conclusion and Next Steps
Requirements
- A solid grasp of core machine learning concepts
- Practical experience in embedded programming
- Proficiency in Python-based data workflows
Target Audience
- AI engineers
- Software developers
- Embedded systems specialists
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 3900 € + VAT*
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