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Duration 21 hours
Course Outline
TinyML Pipeline Foundations
- An overview of the various stages in the TinyML workflow
- Key characteristics of edge hardware
- Essential considerations for pipeline architecture
Data Acquisition and Preprocessing
- Gathering structured and sensor-based data
- Strategies for data labeling and augmentation
- Adapting datasets for resource-constrained environments
Developing TinyML Models
- Choosing model architectures suitable for microcontrollers
- Implementing training workflows with standard ML frameworks
- Assessing key model performance metrics
Optimizing and Compressing Models
- Applying quantization methods
- Utilizing pruning and weight sharing techniques
- Striking a balance between accuracy and resource limitations
Converting and Packaging Models
- Exporting models to TensorFlow Lite
- Incorporating models into embedded toolchains
- Managing model footprint and memory requirements
Deploying to Microcontrollers
- Flashing models onto hardware targets
- Setting up run-time environments
- Conducting real-time inference tests
Monitoring, Testing, and Validation
- Testing methodologies for deployed TinyML systems
- Diagnosing model behavior on hardware
- Verifying performance under field conditions
Assembling the Complete End-to-End Pipeline
- Constructing automated workflows
- Versioning data, models, and firmware
- Overseeing updates and iterative improvements
Summary and Future Directions
Requirements
- A solid grasp of machine learning fundamentals
- Practical experience in embedded programming
- Proficiency with Python-based data workflows
Target Audience
- AI Engineers
- Software Developers
- Embedded Systems Specialists
