Introduction to TinyML Training Course
TinyML refers to the deployment of machine learning techniques on microcontrollers and embedded devices with limited resources.
This guided, live training session (available online or on-site) is designed for engineers and data scientists at the beginner level who want to grasp the core concepts of TinyML, investigate its practical uses, and implement AI models on microcontrollers.
Upon completing this training, participants will be equipped to:
- Grasp the fundamentals of TinyML and why it matters.
- Implement lightweight AI models on microcontrollers and edge devices.
- Optimize and refine machine learning models to minimize power usage.
- Apply TinyML solutions to real-world scenarios, including gesture recognition, anomaly detection, and audio processing.
Course Format
- Interactive lectures and discussions.
- Numerous exercises and practical sessions.
- Live-lab implementation work.
Customization Options
- To request a tailored training session for this course, please reach out to us to make arrangements.
Course Outline
Introduction to TinyML
- What is TinyML?
- The importance of machine learning on microcontrollers
- Comparison between traditional AI and TinyML
- Overview of hardware and software requirements
Setting Up the TinyML Environment
- Installing Arduino IDE and setting up the development environment
- Introduction to TensorFlow Lite and Edge Impulse
- Flashing and configuring microcontrollers for TinyML applications
Building and Deploying TinyML Models
- Understanding the TinyML workflow
- Training a simple machine learning model for microcontrollers
- Converting AI models to TensorFlow Lite format
- Deploying models onto hardware devices
Optimizing TinyML for Edge Devices
- Reducing memory and computational footprint
- Techniques for quantization and model compression
- Benchmarking TinyML model performance
TinyML Applications and Use Cases
- Gesture recognition using accelerometer data
- Audio classification and keyword spotting
- Anomaly detection for predictive maintenance
TinyML Challenges and Future Trends
- Hardware limitations and optimization strategies
- Security and privacy concerns in TinyML
- Future advancements and research in TinyML
Summary and Next Steps
Requirements
- Basic programming skills (Python or C/C++)
- Familiarity with machine learning concepts (recommended but not mandatory)
- Understanding of embedded systems (optional but beneficial)
Target Audience
- Engineers
- Data scientists
- AI enthusiasts
Need help picking the right course?
southafrica@nobleprog.co.za or +27 (0)10 005 5793
Introduction to TinyML Training Course - Enquiry
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<\/p>This instructor-led live training session, available both online and onsite, is designed for senior technical professionals seeking to design, optimize, and implement complete TinyML pipelines.
<\/p>Upon completing this training, participants will be able to:
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