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Course Outline
Foundations of TinyML Workflows
- Overview of the stages within a TinyML workflow
- Key characteristics of edge hardware
- Considerations for pipeline design
Data Collection and Preprocessing
- Gathering structured data and sensor inputs
- Strategies for data labeling and augmentation
- Preparing datasets for resource-constrained environments
Model Development for TinyML
- Choosing model architectures suitable for microcontrollers
- Training workflows utilizing standard ML frameworks
- Assessing model performance metrics
Model Optimization and Compression
- Techniques for quantization
- 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 hardware targets
- Configuring run-time environments
- Conducting real-time inference testing
Monitoring, Testing, and Validation
- Testing strategies for deployed TinyML systems
- Debugging model behavior on hardware
- Validating performance in field conditions
Integrating the Complete End-to-End Pipeline
- Building automated workflows
- Versioning data, models, and firmware
- Managing updates and iterations
Summary and Next Steps
Requirements
- A solid grasp of machine learning fundamentals
- Practical experience with embedded programming
- Familiarity with data workflows based on Python
Target Audience
- AI engineers
- Software developers
- Experts in embedded systems
21 Hours
