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Duration 21 hours
Course Outline
TinyML Workflow Fundamentals
- Survey of TinyML workflow phases
- Attributes of edge hardware
- Key considerations for workflow architecture
Data Acquisition and Preprocessing
- Gathering structured and sensor data
- Strategies for data labeling and augmentation
- Preparing datasets for resource-limited settings
Model Creation for TinyML
- Choosing model architectures for microcontrollers
- Training procedures utilizing standard ML frameworks
- Assessing model performance metrics
Model Refinement and Compression
- Quantization methods
- Pruning and weight sharing techniques
- Balancing accuracy against resource constraints
Model Conversion and Packaging
- Exporting models to TensorFlow Lite
- Integrating models into embedded toolchains
- Addressing model size and memory limitations
Microcontroller Deployment
- Loading models onto hardware targets
- Setting up run-time environments
- Testing real-time inference capabilities
Oversight, Testing, and Verification
- Testing approaches for deployed TinyML systems
- Troubleshooting model behavior on hardware
- Validating performance under field conditions
Assembling the Complete End-to-End Workflow
- Constructing automated processes
- Version control for data, models, and firmware
- Handling updates and iterative improvements
Conclusion and Future Directions
Requirements
- A solid grasp of machine learning core concepts
- Practical experience in embedded programming
- Comfort with Python-based data processing workflows
Target Audience
- AI engineers
- Software developers
- Embedded systems specialists
