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Duration 21 hours
Course Outline
Core Principles of TinyML Pipelines
- Analysis of TinyML workflow phases
- Attributes of edge hardware
- Strategic considerations for pipeline architecture
Data Acquisition and Preparation
- Gathering structured and sensor-based data
- Techniques for data labeling and augmentation
- Adapting datasets for resource-constrained environments
Model Creation for TinyML
- Choosing model architectures suitable for microcontrollers
- Establishing training workflows with standard ML frameworks
- Assessing model performance metrics
Model Refinement and Compression
- Application of quantization methods
- Pruning and weight-sharing strategies
- Balancing precision against resource limitations
Model Transformation and Packaging
- Exporting models to TensorFlow Lite
- Incorporating models into embedded development toolchains
- Managing model footprint and memory usage
Microcontroller Implementation
- Transferring models to hardware targets
- Setting up run-time environments
- Conducting real-time inference tests
Monitoring, Verification, and Validation
- Validation approaches for deployed TinyML systems
- Diagnosing model behavior on physical hardware
- Confirming performance under field conditions
Assembling the Complete End-to-End Pipeline
- Creating automated workflows
- Version controlling data, models, and firmware
- Overseeing updates and iterative improvements
Conclusion and Future Directions
Requirements
- Foundational knowledge of machine learning principles
- Proficiency in embedded programming
- Aptitude with Python-driven data processing workflows
Target Audience
- AI Engineers
- Software Developers
- Embedded Systems Specialists
