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Duration 21 hours
Course Outline
TinyML Pipeline Foundations
- Overview of TinyML workflow stages
- Key characteristics of edge hardware
- Considerations for pipeline design
Data Acquisition and Preprocessing
- Gathering structured and sensor data
- Strategies for data labeling and augmentation
- Preparing datasets for resource-constrained environments
Model Development for TinyML
- Selecting 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 against 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 hardware targets
- Configuring run-time environments
- Testing real-time inference
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 solid grasp of machine learning fundamentals
- Experience with embedded programming
- Proficiency in Python-based data workflows
Target Audience
- AI Engineers
- Software Developers
- Embedded Systems Specialists
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 4800 € + VAT*
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