TinyML Training in Bhutan

This instructor-led, live training in Bhutan (online or onsite) is aimed at 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 instructor-led program in Bhutan empowers advanced professionals with the capabilities to design, optimize, and deploy full TinyML pipelines. Learners will master data collection, training of low-power models, and real-world application testing through hands-on labs.

This instructor-led training in Bhutan empowers advanced professionals to secure TinyML pipelines on edge devices. The curriculum covers the implementation of privacy-preserving techniques, hardening models against adversarial threats, and applying best practices for secure data handling in constrained environments.

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

This 21-hour instructor-led course in Bhutan enables intermediate professionals to effectively deploy TinyML for smart agriculture. Participants will learn to build lightweight models, integrate edge AI with IoT systems, and optimize solutions for precision irrigation and pest detection within a hands-on lab setting.

This instructor-led live training in Bhutan focuses on deploying TinyML solutions for healthcare monitoring and diagnostics. Learners will master the design of models for real-time health data, optimize for low-power wearables, and ensure clinical reliability. The course includes practical lab exercises.

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

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

Upon completion 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.
• Create 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 instructor-led, live training in Bhutan (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.

Upon completing this training, participants will be able to:

• Comprehend the core principles of TinyML and its advantages for edge AI applications.
• Configure a development environment suitable for TinyML projects.
• Train, optimize, and deploy AI models on low-power microcontrollers.
• Utilize TensorFlow Lite and Edge Impulse to build real-world TinyML applications.
• Enhance AI models for power efficiency and manage memory constraints effectively.

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

Upon completion of this training, participants will be able to:

• Grasp the core concepts of TinyML and its importance.
• Deploy lightweight AI models on microcontrollers and edge devices.
• Optimize and fine-tune machine learning models to minimize power usage.
• Implement TinyML in real-world scenarios such as gesture recognition, anomaly detection, and audio processing.