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.

TinyML refers to a machine learning methodology fine-tuned for compact, resource-limited devices.

This trainer-led live session, available online or at your venue, is designed for learners at beginner to intermediate levels who aim to develop functional TinyML applications using Raspberry Pi, Arduino, and comparable microcontrollers.

After finishing this training, participants will acquire the ability to:

• Gather and prepare data specifically for TinyML initiatives.
• Train and optimize compact machine learning models for microcontroller settings.
• Deploy TinyML models onto Raspberry Pi, Arduino, and associated boards.
• Create complete end-to-end embedded AI prototypes.

Course Format

• Trainer-led lectures and guided discussions.
• Practical activities and hands-on experimentation.
• Live-lab project work involving actual hardware.

Customization Options for the Course

• For bespoke training that aligns with your specific hardware or application requirements, please reach out to us to make arrangements.

TinyML involves the deployment of optimized machine learning models onto edge devices with limited resources.

This instructor-led live training, available both online and onsite, targets advanced technical professionals aiming to design, optimize, and implement comprehensive TinyML pipelines.

Upon completing this training, participants will acquire the skills to:

• Gather, prepare, and manage datasets for TinyML use cases.
• Train and fine-tune models for low-power microcontrollers.
• Transform models into lightweight formats compatible with edge devices.
• Deploy, test, and monitor TinyML applications on actual hardware.

Course Delivery Format

• Lectures guided by instructors alongside technical discussions.
• Practical lab sessions and iterative experimentation.
• Hands-on deployment on microcontroller-based platforms.

Customization Options

• For tailored training involving specific toolchains, hardware boards, or internal workflows, please reach out to us to make arrangements.

TinyML involves deploying machine learning models on low-power, resource-constrained devices at the network edge.

This instructor-led live training, available both online and onsite, is designed for advanced professionals aiming to secure TinyML pipelines and integrate privacy-preserving techniques into edge AI applications.

Upon completing this course, participants will be capable of:

• Identifying security risks specific to on-device TinyML inference.
• Implementing privacy-preserving mechanisms for edge AI deployments.
• Strengthening TinyML models and embedded systems against adversarial threats.
• Applying best practices for secure data handling in constrained environments.

Course Format

• Interactive lectures accompanied by expert-led discussions.
• Practical exercises focusing on real-world threat scenarios.
• Hands-on implementation using embedded security tools and TinyML frameworks.

Course Customization Options

• Organizations can request a customized version of this training to meet their specific security and compliance requirements.

TinyML serves as a framework for deploying machine learning models onto low-power microcontrollers and embedded platforms within robotics and autonomous systems.

This instructor-led live training, available online or onsite, targets advanced professionals seeking to incorporate TinyML-based perception and decision-making capabilities into autonomous robots, drones, and intelligent control systems.

After completing this course, participants will be equipped to:

• Design optimized TinyML models tailored for robotics applications.
• Implement on-device perception pipelines for real-time autonomy.
• Integrate TinyML into existing robotic control frameworks.
• Deploy and test lightweight AI models on embedded hardware platforms.

Format of the Course

• Technical lectures combined with interactive discussions.
• Hands-on labs focusing on embedded robotics tasks.
• Practical exercises simulating real-world autonomous workflows.

Course Customization Options

• For organization-specific robotics environments, customization can be arranged upon request.

TinyML enables the deployment of machine learning models on low-power, resource-constrained devices directly in the field.

This instructor-led live training, available online or onsite, is designed for intermediate-level professionals looking to apply TinyML techniques to smart agriculture solutions that enhance automation and environmental intelligence.

Upon completing this program, participants will be able to:

• Build and deploy TinyML models for agricultural sensing applications.
• Integrate edge AI into IoT ecosystems for automated crop monitoring.
• Utilize specialized tools to train and optimize lightweight models.
• Develop workflows for precision irrigation, pest detection, and environmental analytics.

Format of the Course

• Guided presentations coupled with applied technical discussions.
• Hands-on practice using real-world datasets and devices.
• Practical experimentation in a supported lab environment.

Course Customization Options

• For tailored training aligned with specific agricultural systems, please contact us to customize the program.

TinyML refers to the integration of machine learning capabilities into low-power, resource-constrained wearable and medical devices.

This instructor-led training session, available both online and onsite, is designed for intermediate-level professionals aiming to implement TinyML solutions for healthcare monitoring and diagnostic applications.

Upon completion of this course, participants will be equipped to:

• Design and deploy TinyML models for real-time health data processing.
• Collect, preprocess, and interpret biosensor data to derive AI-driven insights.
• Optimize models specifically for low-power and memory-constrained wearable devices.
• Assess the clinical relevance, reliability, and safety of outputs generated by TinyML.

Course Format

• Lectures supplemented with live demonstrations and interactive discussions.
• Practical exercises involving wearable device data and TinyML frameworks.
• Guided implementation exercises within a lab environment.

Customization Options

• For training tailored to specific healthcare devices or regulatory workflows, please contact us to customize the program.

TinyML involves the deployment of machine learning models onto hardware with stringent resource limitations.

This instructor-led, live training (available online or onsite) is designed for advanced practitioners seeking to optimize TinyML models for low-latency, memory-efficient deployment on embedded devices.

Upon completing this training, participants will be able to:

• Utilize quantization, pruning, and compression methods to minimize model size while preserving accuracy.
• Benchmark TinyML models to assess latency, memory usage, and energy efficiency.
• Deploy optimized inference pipelines on microcontrollers and edge devices.
• Assess the trade-offs between performance, accuracy, and hardware constraints.

Course Format

• Instructor-led presentations complemented by technical demonstrations.
• Practical optimization exercises and comparative performance testing.
• Hands-on implementation of TinyML pipelines within a controlled lab environment.

Customization Options

• For training tailored to specific hardware platforms or internal workflows, please contact us to customize the program.

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.