Building End-to-End TinyML Pipelines Training Course
TinyML is the practice of deploying optimized machine learning models on resource-constrained edge devices.
This instructor-led, live training (online or onsite) is aimed at advanced-level technical professionals who wish to design, optimize, and deploy complete TinyML pipelines.
By the conclusion of this training, participants will learn how to:
- Collect, prepare, and manage datasets for TinyML applications.
- Train and optimize models for low-power microcontrollers.
- Convert models to lightweight formats suitable for edge devices.
- Deploy, test, and monitor TinyML applications in real hardware environments.
Format of the Course
- Instructor-guided lectures and technical discussion.
- Practical labs and iterative experimentation.
- Hands-on deployment on microcontroller-based platforms.
Course Customization Options
- To customize the training with specific toolchains, hardware boards, or internal workflows, please contact us to arrange.
Course Outline
Foundations of TinyML Pipelines
- Overview of TinyML workflow stages
- Characteristics of edge hardware
- Pipeline design considerations
Data Collection and Preprocessing
- Collecting structured and sensor data
- Data labeling and augmentation strategies
- Preparing datasets for constrained environments
Model Development for TinyML
- Selecting model architectures for microcontrollers
- Training workflows using standard ML frameworks
- Evaluating model performance indicators
Model Optimization and Compression
- Quantization techniques
- Pruning and weight sharing
- Balancing accuracy and resource limits
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
- Real-time inference testing
Monitoring, Testing, and Validation
- Testing strategies for deployed TinyML systems
- Debugging model behavior on hardware
- Performance validation 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
- An understanding of machine learning fundamentals
- Experience with embedded programming
- Familiarity with Python-based data workflows
Audience
- AI engineers
- Software developers
- Embedded systems experts
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