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Duration 21 hours
Course Outline
Foundations of TinyML Pipelines
- Overview of TinyML workflow stages
- Key characteristics of edge hardware
- Strategic pipeline design considerations
Data Collection and Preprocessing
- Gathering structured and sensor-derived data
- Strategies for data labeling and augmentation
- Preparing datasets for resource-constrained environments
Model Development for TinyML
- Selecting appropriate model architectures for microcontrollers
- Training workflows utilizing standard ML frameworks
- Evaluating key model performance indicators
Model Optimization and Compression
- Application of quantization techniques
- Pruning and weight sharing methods
- Balancing model accuracy against resource limits
Model Conversion and Packaging
- Exporting models to TensorFlow Lite
- Integrating models into embedded toolchains
- Managing model size and memory constraints
Deployment on Microcontrollers
- Flashing models onto hardware targets
- Configuring run-time environments
- Conducting real-time inference tests
Monitoring, Testing, and Validation
- Testing strategies for deployed TinyML systems
- Debugging model behavior on hardware
- Validating performance in 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
- Proficiency in machine learning fundamentals
- Practical experience with embedded programming
- Knowledge of Python-based data workflows
Target Audience
- AI Engineers
- Software Developers
- Embedded Systems Specialists
