TinyML with Raspberry Pi and Arduino Training Course

This expert-led course in South Korea empowers advanced professionals to architect, optimize, and execute full-scale TinyML workflows. Through practical lab environments, participants will master data acquisition, low-power model training, and real-world application testing.

This instructor-led, live training in South Korea (online or onsite) is designed for intermediate-level embedded systems engineers and AI developers who want to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.

Upon completing this training, participants will be able to:

• Grasp the core concepts of TinyML and its advantages for edge AI applications.
• Establish a development environment tailored for TinyML projects.
• Train, optimize, and deploy AI models on low-power microcontrollers.
• Leverage TensorFlow Lite and Edge Impulse to build real-world TinyML solutions.
• Enhance AI models for improved power efficiency and to meet memory limitations.

This live, instructor-led training in South Korea empowers advanced practitioners to optimize TinyML models for resource-limited embedded devices. Learners will apply quantization and pruning, develop low-latency inference pipelines, and evaluate performance against memory and energy constraints.

This instructor-led training in South Korea empowers advanced professionals to secure TinyML pipelines on edge devices. You will acquire the skills to implement privacy-preserving techniques, fortify models against adversarial threats, and apply best practices for secure data management in constrained environments.

This instructor-led, live training in South Korea (online or onsite) is designed for beginner-level engineers and data scientists seeking to grasp the fundamentals of TinyML, explore its practical applications, and deploy AI models on microcontrollers.

Upon completing this training, participants will be able to:

• Comprehend the core principles of TinyML and its importance.
• Deploy lightweight AI models on microcontrollers and edge devices.
• Optimize and fine-tune machine learning models to minimize power consumption.
• Utilize TinyML for real-world scenarios such as gesture recognition, anomaly detection, and audio processing.

This instructor-led, live training in South Korea guides advanced professionals on integrating TinyML into autonomous robotics. Participants will design optimized models, implement on-device perception pipelines, and deploy lightweight AI on embedded hardware to achieve real-time autonomy.

This instructor-led, live training in South Korea (online or onsite) is designed for intermediate-level embedded engineers, IoT developers, and AI researchers who wish to implement TinyML techniques for AI-powered applications on energy-efficient hardware.

By the end of this training, participants will be able to:

• Understand the fundamentals of TinyML and edge AI.
• Deploy lightweight AI models on microcontrollers.
• Optimize AI inference for low-power consumption.
• Integrate TinyML with real-world IoT applications.

This live, instructor-led training in South Korea focuses on implementing TinyML solutions for healthcare monitoring and diagnostics. Participants will learn to design models for real-time health data, optimize them for low-power wearables, and ensure clinical reliability, supported by hands-on lab exercises.

This instructor-led, live training in South Korea (online or onsite) is designed for intermediate-level IoT developers, embedded engineers, and AI practitioners who want to implement TinyML for predictive maintenance, anomaly detection, and smart sensor applications.

By the end of this training, participants will be able to:

• Grasp the fundamentals of TinyML and its applications in IoT.
• Set up a TinyML development environment for IoT projects.
• Develop and deploy ML models on low-power microcontrollers.
• Implement predictive maintenance and anomaly detection using TinyML.
• Optimize TinyML models for efficient power and memory usage.

This 21-hour instructor-led course in South Korea empowers intermediate professionals to deploy TinyML for smart agriculture. Learn to build lightweight models, integrate edge AI with IoT, and optimize for precision irrigation and pest detection in a hands-on lab setting.