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Duration 21 hours
Course Outline
Core Principles of TinyML Workflows
- Introduction to the stages of the TinyML lifecycle
- Key attributes of edge hardware
- Strategic considerations for pipeline architecture
Data Acquisition and Refinement
- Gathering structured and sensor-based data
- Strategies for labeling and data augmentation
- Adapting datasets for resource-limited environments
Model Construction for TinyML
- Choosing appropriate architectures for microcontrollers
- Training procedures using standard ML frameworks
- Assessing key model performance metrics
Model Optimization and Compression
- Application of quantization methods
- Techniques for pruning and weight sharing
- Striking a balance between precision and resource availability
Model Transformation and Packaging
- Converting models for TensorFlow Lite
- Incorporating models into embedded development toolchains
- Addressing model size and memory limitations
Implementation on Microcontrollers
- Writing models to hardware targets
- Setting up runtime environments
- Conducting real-time inference assessments
Oversight, Testing, and Validation
- Testing methodologies for deployed TinyML systems
- Troubleshooting model performance on physical hardware
- Verifying performance under field conditions
Assembling the Complete End-to-End Workflow
- Creating automated processing pipelines
- Managing versions of data, models, and firmware
- Overseeing updates and iterative improvements
Recap and Future Directions
Requirements
- A solid grasp of core machine learning principles
- Practical experience in embedded programming
- Proficiency in Python-driven data workflows
Target Audience
- AI engineers
- Software developers
- Embedded systems specialists
