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Duration 21 hours
Course Outline
TinyML Pipeline Fundamentals
- Overview of TinyML workflow phases
- Attributes of edge hardware
- Key considerations in pipeline design
Data Acquisition and Preparation
- Acquiring structured and sensor-based data
- Strategies for data labeling and augmentation
- Refining datasets for resource-limited environments
TinyML Model Development
- Choosing model architectures suitable for microcontrollers
- Training processes using standard ML frameworks
- Assessing key model performance metrics
Model Optimization and Reduction
- Techniques for quantization
- Methods for pruning and weight sharing
- Striking a balance between accuracy and resource constraints
Model Translation and Packaging
- Exporting models to TensorFlow Lite
- Integrating models into embedded development toolchains
- Managing model size and memory limitations
Microcontroller Deployment
- Flashing models to hardware targets
- Setting up run-time environments
- Testing real-time inference capabilities
Monitoring, Testing, and Verification
- Strategies for testing deployed TinyML systems
- Debugging model behavior on physical hardware
- Validating performance under field conditions
Integrating the Complete End-to-End Pipeline
- Creating automated workflows
- Versioning data, models, and firmware
- Managing updates and iterative improvements
Recap and Future Directions
Requirements
- Grasping of core machine learning concepts
- Hands-on experience in embedded programming
- Proficiency with Python-centric data workflows
Intended Audience
- AI engineers
- Software developers
- Embedded systems specialists
