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Duration 21 hours
Course Outline
Basics of TinyML Workflows
- Introduction to the various stages of the TinyML process
- Key attributes of edge hardware
- Considerations for pipeline architecture
Data Gathering and Preprocessing
- Acquiring structured and sensor-based data
- Strategies for data labeling and augmentation
- Preparing datasets for resource-limited environments
Model Creation for TinyML
- Choosing model architectures suitable for microcontrollers
- Training procedures using mainstream ML frameworks
- Assessing key model performance metrics
Model Refinement and Compression
- Quantization methods
- Pruning and weight sharing techniques
- Striking a balance between accuracy and resource constraints
Model Transformation and Packaging
- Converting models to TensorFlow Lite format
- Incorporating models into embedded development toolchains
- Managing model size and memory usage
Implementation on Microcontrollers
- Loading models onto specific hardware targets
- Setting up run-time configurations
- Conducting real-time inference tests
Monitoring, Testing, and Validation
- Approaches for testing deployed TinyML systems
- Troubleshooting model behavior on physical hardware
- Validating performance in field environments
Assembling the Complete End-to-End Pipeline
- Creating automated workflows
- Implementing version control for data, models, and firmware
- Overseeing updates and iterative improvements
Recap and Future Directions
Requirements
- A solid grasp of machine learning basics
- Hands-on experience with embedded programming
- Knowledge of Python-based data processing workflows
Intended Learners
- AI Engineers
- Software Developers
- Embedded Systems Specialists
