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Course Outline
Foundations of TinyML Pipelines
- Overview of the TinyML workflow stages
- Key characteristics of edge hardware
- Critical considerations for pipeline design
Data Collection and Preprocessing
- Acquiring structured and sensor data
- Strategies for data labeling and augmentation
- Preparing datasets for resource-constrained environments
Model Development for TinyML
- Choosing appropriate model architectures for microcontrollers
- Training workflows utilizing standard ML frameworks
- Evaluating key performance indicators of models
Model Optimization and Compression
- Techniques for quantization
- Pruning methods and weight sharing
- Balancing model accuracy with resource limitations
Model Conversion and Packaging
- Exporting models to TensorFlow Lite format
- Integrating models into embedded toolchains
- Managing model size and memory constraints
Deployment on Microcontrollers
- Flashing models onto target hardware
- Configuring run-time environments
- Conducting real-time inference testing
Monitoring, Testing, and Validation
- Testing strategies for deployed TinyML systems
- Debugging model behavior directly on hardware
- Validating performance under real-world field conditions
Integrating the Full End-to-End Pipeline
- Constructing automated workflows
- Versioning data, models, and firmware
- Managing updates and iterative improvements
Summary and Next Steps
Requirements
- A solid grasp of machine learning fundamentals
- Prior experience in embedded programming
- Familiarity with Python-based data processing workflows
Target Audience
- AI engineers
- Software developers
- Embedded systems experts
21 Hours
