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Duration 21 hours
Course Outline
TinyML Pipeline Fundamentals
- Summary of TinyML workflow phases
- Attributes of edge hardware
- Strategic considerations for pipeline design
Data Acquisition and Preprocessing
- Gathering structured and sensor data
- Strategies for data labeling and augmentation
- Preparing datasets for resource-constrained settings
Developing Models for TinyML
- Choosing model architectures suitable for microcontrollers
- Training processes using standard ML frameworks
- Assessing model performance metrics
Optimizing and Compressing Models
- Quantization methods
- Pruning and weight sharing techniques
- Balancing accuracy against 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
- Loading models onto hardware targets
- Setting up run-time environments
- Testing real-time inference
Monitoring, Testing, and Validation
- Testing approaches for deployed TinyML systems
- Debugging model behavior on hardware
- Validating performance in field conditions
Integrating the Complete End-to-End Pipeline
- Creating automated workflows
- Versioning data, models, and firmware
- Managing updates and iterations
Summary and Next Steps
Requirements
- A solid grasp of machine learning fundamentals
- Proficiency in embedded programming
- Knowledge of Python-based data workflows
Target Audience
- AI engineers
- Software developers
- Embedded systems specialists
