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Course Outline
Foundations of TinyML Pipelines
- Overview of TinyML workflow stages
- Characteristics of edge hardware
- Pipeline design considerations
Data Collection and Preprocessing
- Collecting structured and sensor data
- Data labeling and augmentation strategies
- Preparing datasets for constrained environments
Model Development for TinyML
- Selecting model architectures for microcontrollers
- Training workflows using standard ML frameworks
- Evaluating model performance indicators
Model Optimization and Compression
- Quantization techniques
- Pruning and weight sharing
- Balancing accuracy and resource limits
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 hardware targets
- Configuring run-time environments
- Real-time inference testing
Monitoring, Testing, and Validation
- Testing strategies for deployed TinyML systems
- Debugging model behavior on hardware
- Performance validation in field conditions
Integrating the Full End-to-End Pipeline
- Building automated workflows
- Versioning data, models, and firmware
- Managing updates and iterations
Summary and Next Steps
Requirements
- An understanding of machine learning fundamentals
- Experience with embedded programming
- Familiarity with Python-based data workflows
Audience
- AI engineers
- Software developers
- Embedded systems experts
21 Hours
