Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
TinyML Pipeline Fundamentals
- Overview of TinyML workflow stages
- Characteristics of edge hardware
- Key considerations in pipeline design
Data Collection and Preprocessing
- Gathering structured and sensor data
- Strategies for data labeling and augmentation
- Preparing datasets for resource-constrained environments
Model Development for TinyML
- Choosing model architectures suitable for microcontrollers
- Training workflows using standard ML frameworks
- Evaluating model performance metrics
Model Optimization and Compression
- Quantization techniques
- Pruning and weight sharing methods
- Balancing accuracy with 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
- Flashing models onto target hardware
- 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
- Building automated workflows
- Versioning data, models, and firmware
- Managing updates and iterations
Summary and Next Steps
Requirements
- A foundational understanding of machine learning concepts
- Experience in embedded programming
- Familiarity with Python-based data processing workflows
Target Audience
- AI engineers
- Software developers
- Embedded systems experts
21 Hours
