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Course Outline
TinyML Pipeline Fundamentals
- Insight into TinyML workflow phases
- Attributes of edge hardware
- Strategic considerations for pipeline architecture
Data Acquisition and Preparation
- Gathering structured and sensor data
- Techniques for data labelling and augmentation
- Adapting datasets for resource-limited environments
TinyML Model Creation
- Choosing model architectures for microcontrollers
- Training procedures using standard ML frameworks
- Assessing model performance metrics
Model Refinement and Compression
- Quantisation methods
- Pruning and weight sharing techniques
- Striking a balance between accuracy and resource limitations
Model Translation and Packaging
- Exporting models to TensorFlow Lite
- Integrating models within embedded toolchains
- Managing model footprint and memory usage
Microcontroller Deployment
- Writing models to hardware targets
- Setting up run-time environments
- Testing real-time inference capabilities
Monitoring, Testing, and Verification
- Test methodologies for deployed TinyML systems
- Debugging model behaviour on hardware
- Validating performance in field conditions
Assembling the Complete End-to-End Pipeline
- Creating automated workflows
- Version control for data, models, and firmware
- Overseeing updates and iterations
Conclusion and Future Directions
Requirements
- A solid grasp of core machine learning concepts
- Proficiency in embedded programming
- Knowledge of Python-centric data workflows
Target Audience
- AI engineers
- Software developers
- Embedded systems specialists
21 Hours