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Duration 21 hours
Course Outline
Core Principles of TinyML Pipelines
- Introduction to the stages of a TinyML workflow
- Key attributes of edge hardware
- Strategic considerations for pipeline architecture
Data Acquisition and Preparation
- Gathering structured and sensor-derived data
- Techniques for data labeling and augmentation
- Adapting datasets for constrained computing environments
TinyML Model Development
- Choosing appropriate architectures for microcontrollers
- Training processes within standard ML frameworks
- Assessing key model performance metrics
Model Refinement and Reduction
- Applying quantization methods
- Implementing pruning and weight sharing
- Striking a balance between accuracy and resource limitations
Model Export and Packaging
- Converting models to TensorFlow Lite
- Embedding models within embedded toolchains
- Addressing model size and memory restrictions
Microcontroller Deployment
- Installing models onto hardware targets
- Setting up runtime environments
- Conducting real-time inference tests
Monitoring, Evaluation, and Verification
- Strategies for testing deployed TinyML systems
- Troubleshooting model behavior on physical hardware
- Validating performance under field conditions
Assembling the Complete End-to-End Pipeline
- Creating automated workflows
- Versioning data, models, and firmware
- Overseeing updates and iterative improvements
Conclusion and Future Directions
Requirements
- Core knowledge of machine learning principles
- Proficiency in embedded programming
- Experience with Python-based data processing workflows
Target Audience
- AI Engineers
- Software Developers
- Embedded Systems Specialists
