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Duration 21 hours
Course Outline
Core Principles of TinyML Workflows
- Overview of the TinyML process stages
- Key attributes of edge hardware
- Considerations for workflow architecture
Data Acquisition and Preparation
- Gathering structured and sensor-based data
- Strategies for data labeling and augmentation
- Adapting datasets for resource-limited environments
Creating Models for TinyML
- Choosing model architectures for microcontrollers
- Training processes using standard ML frameworks
- Assessing model performance metrics
Optimizing and Compressing Models
- Application of quantization methods
- Pruning and weight sharing techniques
- Reconciling accuracy with resource constraints
Model Export and Packaging
- Converting models to TensorFlow Lite
- Incorporating models into embedded development toolchains
- Addressing model size and memory limitations
Implementation on Microcontrollers
- Programming models onto hardware targets
- Setting up runtime environments
- Conducting real-time inference assessments
Oversight, Verification, and Confirmation
- Testing methodologies for deployed TinyML systems
- Troubleshooting model behavior on physical hardware
- Validating performance under field conditions
Assembling the Complete End-to-End Workflow
- Creating automated processes
- Managing versions of data, models, and firmware
- Handling updates and iterative improvements
Recap and Future Directions
Requirements
- Grasp of machine learning core concepts
- Background in embedded coding
- Knowledge of Python-centric data processes
Target Audience
- AI specialists
- Software engineers
- Embedded systems professionals
