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Duration 21 hours
Course Outline
Foundations of TinyML Pipelines
- Overview of the stages in a TinyML workflow
- Key attributes of edge hardware
- Considerations for pipeline design
Data Acquisition and Preprocessing
- Gathering structured and sensor data
- Strategies for data labeling and augmentation
- Preparing datasets for resource-constrained environments
Developing Models for TinyML
- Selecting suitable model architectures for microcontrollers
- Training workflows using standard ML frameworks
- Evaluating key model performance metrics
Model Optimisation and Compression
- Quantization techniques
- Pruning and weight sharing methods
- Balancing accuracy against resource limitations
Model Conversion and Packaging
- Exporting models to TensorFlow Lite
- Integrating models into embedded toolchains
- Managing model size and memory constraints
Deployment on Microcontrollers
- Flashing models onto target hardware
- Configuring run-time environments
- Testing real-time inference
Monitoring, Testing, and Validation
- Testing strategies for deployed TinyML systems
- Debugging model behaviour on hardware
- Performance validation under field conditions
Integrating the Full End-to-End Pipeline
- Creating automated workflows
- Versioning data, models, and firmware
- Managing updates and iterations
Summary and Next Steps
Requirements
- A solid understanding of machine learning fundamentals
- Experience with embedded programming
- Familiarity with Python-based data workflows
Target Audience
- AI engineers
- Software developers
- Embedded systems specialists
