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Course Outline
Foundations of TinyML Pipelines
- Overview of TinyML workflow stages.
- Characteristics of edge hardware.
- Key considerations in pipeline design.
Data Collection and Preprocessing
- Gathering structured and sensor data.
- Strategies for data labeling and augmentation.
- Preparing datasets for resource-constrained environments.
Model Development for TinyML
- Selecting model architectures suitable for microcontrollers.
- Training workflows using standard ML frameworks.
- Evaluating model performance indicators.
Model Optimization and Compression
- Quantization techniques.
- Pruning and weight sharing methods.
- Balancing accuracy with 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 under 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
- 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 experts.
21 Hours
