TinyML with Raspberry Pi and Arduino Training Course

This instructor-led session in Vietnam empowers advanced professionals to design, optimize, and deploy complete TinyML pipelines. Learners will master data collection, low-power model training, and real-world application testing through practical laboratory work.

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

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

• Understand the fundamental principles of TinyML and its benefits for edge AI applications.
• Establish a development environment suitable for TinyML projects.
• Train, optimize, and deploy AI models on low-power microcontrollers.
• Apply TensorFlow Lite and Edge Impulse to build real-world TinyML solutions.
• Optimize AI models for power efficiency and memory constraints.

This live, instructor-led training in Vietnam enables advanced practitioners to optimize TinyML models for resource-limited embedded devices. Participants will apply quantization and pruning, build low-latency inference pipelines, and benchmark performance against memory and energy constraints.

This instructor-led training in Vietnam empowers advanced professionals to secure TinyML pipelines on edge devices. You will learn to implement privacy-preserving techniques, reinforce models against adversarial threats, and apply best practices for secure data handling in constrained environments.

This instructor-led, live training in Vietnam (available online or onsite) targets beginner-level engineers and data scientists seeking to understand TinyML fundamentals, explore its applications, and deploy AI models on microcontrollers.

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

• Understand the fundamentals of TinyML and its significance.
• Deploy lightweight AI models on microcontrollers and edge devices.
• Optimize and fine-tune machine learning models for low-power consumption.
• Apply TinyML for real-world applications such as gesture recognition, anomaly detection, and audio processing.

This live, instructor-led training in Vietnam guides advanced professionals in integrating TinyML into autonomous robotics. Learners 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 Vietnam (online or onsite) is designed for intermediate-level embedded engineers, IoT developers, and AI researchers who wish to apply TinyML techniques for AI-driven applications on energy-efficient hardware.

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

• Grasp 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 Vietnam focuses on implementing TinyML solutions for healthcare monitoring and diagnostics. Participants will learn to design models for real-time health data analysis, optimize them for low-power wearable devices, and ensure clinical reliability. The course includes practical lab exercises to reinforce learning.

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

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

• Understand 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 Vietnam 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.