TinyML: Running AI on Ultra-Low-Power Edge Devices Training Course

This instructor-led, live training in South Korea (online or onsite) is aimed at intermediate-level telecom professionals, AI engineers, and IoT specialists who wish to explore how 5G networks accelerate Edge AI applications.

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

• Understand the fundamentals of 5G technology and its impact on Edge AI.
• Deploy AI models optimized for low-latency applications in 5G environments.
• Implement real-time decision-making systems using Edge AI and 5G connectivity.
• Optimize AI workloads for efficient performance on edge devices.

This forward-looking course examines the convergence of 6G wireless technologies with edge computing, IoT ecosystems, and AI-powered data processing to build intelligent, adaptive, and low-latency infrastructures.

Designed for intermediate-level IT architects, this instructor-led live training (available online or onsite) helps participants understand and design next-generation distributed architectures that leverage the synergy between 6G connectivity and intelligent edge systems.

Upon completion, participants will be able to:

• Grasp how 6G will reshape edge computing and IoT architectures.
• Design distributed systems optimized for ultra-low latency, high bandwidth, and autonomous operations.
• Integrate AI and data analytics at the edge to enable intelligent decision-making.
• Plan scalable, secure, and resilient infrastructures ready for 6G adoption.
• Evaluate the business and operational models facilitated by the 6G-edge convergence.

Course Format

• Interactive lectures and guided discussions.
• Case studies and applied architecture design exercises.
• Hands-on simulation using optional edge or container tools.

Customization Options

• To request customized training for this course, please contact us to arrange.

This instructor-led, live training in South Korea (online or onsite) is designed for advanced-level AI practitioners, researchers, and developers who wish to master the latest advancements in Edge AI, optimize their AI models for edge deployment, and explore specialized applications across various industries.

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

• Investigate advanced techniques in the development and optimization of Edge AI models.
• Apply innovative strategies for deploying AI models on edge devices.
• Leverage specialized tools and frameworks for advanced Edge AI applications.
• Enhance the performance and efficiency of Edge AI solutions.
• Discover innovative use cases and emerging trends in Edge AI.
• Tackle advanced ethical and security challenges in Edge AI deployments.

This instructor-led, live training in South Korea (online or onsite) is aimed at intermediate-level developers, data scientists, and tech enthusiasts who wish to gain practical skills in deploying AI models on edge devices for various applications.

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

• Understand the principles of Edge AI and its benefits.
• Set up and configure the edge computing environment.
• Develop, train, and optimize AI models for edge deployment.
• Implement practical AI solutions on edge devices.
• Evaluate and improve the performance of edge-deployed models.
• Address ethical and security considerations in Edge AI applications.

TinyML involves deploying optimized machine learning models onto edge devices with limited resources.

This instructor-led live training, available online or onsite, is designed for advanced technical professionals who aim to design, optimize, and implement complete TinyML pipelines.

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

• Gather, preprocess, and manage datasets tailored for TinyML applications.
• Train and optimize models for low-power microcontrollers.
• Transform models into lightweight formats compatible with edge devices.
• Deploy, test, and monitor TinyML applications on actual hardware.

Course Format

• Instructor-led lectures combined with technical discussions.
• Practical labs and iterative experimentation.
• Hands-on deployment on microcontroller-based platforms.

Customization Options

• To tailor the training to specific toolchains, hardware boards, or internal workflows, please contact us to arrange.

This instructor-led, live training in South Korea (online or onsite) is designed for advanced-level cybersecurity professionals, AI engineers, and IoT developers who wish to implement robust security measures and resilience strategies for Edge AI systems.

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

• Comprehend the security risks and vulnerabilities associated with Edge AI deployments.
• Apply encryption and authentication techniques to ensure data protection.
• Design resilient Edge AI architectures capable of withstanding cyber threats.
• Execute secure AI model deployment strategies within edge environments.

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.

TinyML refers to the practice of deploying machine learning models on highly resource-constrained hardware.

This instructor-led, live training (online or onsite) is aimed at advanced-level practitioners who wish to optimize TinyML models for low-latency, memory-efficient deployment on embedded devices.

Upon completing this training, participants will be able to:

• Apply quantization, pruning, and compression techniques to reduce model size without sacrificing accuracy.
• Benchmark TinyML models for latency, memory consumption, and energy efficiency.
• Implement optimized inference pipelines on microcontrollers and edge devices.
• Evaluate trade-offs between performance, accuracy, and hardware constraints.

Format of the Course

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

Course Customization Options

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

TinyML represents a methodology for deploying machine learning models onto low-power, resource-constrained devices operating at the network edge.

This instructor-led, live training (available online or onsite) targets advanced-level professionals seeking to secure TinyML pipelines and implement privacy-preserving techniques within edge AI applications.

Upon completion of this course, participants will be capable of:

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

Format of the Course

• Engaging lectures supported by expert-led discussions.
• Practical exercises emphasizing real-world threat scenarios.
• Hands-on implementation using embedded security and TinyML tooling.

Course Customization Options

• Organizations may request a tailored version of this training to align with their specific security and compliance needs.

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.

TinyML enables the deployment of machine learning models on low-power microcontrollers and embedded platforms, which are essential components in robotics and autonomous systems.

This instructor-led live training, available both online and onsite, is designed for advanced professionals aiming to incorporate TinyML-based perception and decision-making 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 to achieve real-time autonomy.
• Integrate TinyML solutions 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 integrates machine learning into low-power, resource-constrained wearable and medical devices.

This instructor-led, live training (available online or onsite) targets intermediate-level practitioners looking to implement TinyML solutions for healthcare monitoring and diagnostic applications.

Upon completion, participants will be able to:

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

Course Format

• Lectures supplemented by live demonstrations and interactive discussions.
• Hands-on practice utilizing wearable device data and TinyML frameworks.
• Implementation exercises conducted in a guided lab environment.

Customization Options

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

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.

TinyML represents a machine learning methodology specifically designed for small devices with limited resources.

This instructor-led live training, available online or on-site, targets beginner to intermediate learners interested in creating functional TinyML applications using Raspberry Pi, Arduino, and comparable microcontrollers.

Upon finishing this course, participants will acquire the ability to:

• Gather and preprocess data for TinyML initiatives.
• Train and optimize compact machine learning models for microcontroller environments.
• Deploy TinyML models onto Raspberry Pi, Arduino, and associated development boards.
• Construct end-to-end embedded AI prototypes.

Course Format

• Instructor-led lectures and guided discussions.
• Practical exercises and hands-on experimentation.
• Live-lab projects conducted on actual hardware.

Customization Options

• For training tailored to your specific hardware or use case, please reach out to us to arrange accordingly.

TinyML serves as a framework for deploying machine learning models on low-power, resource-constrained devices in the field.

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

Upon completing this program, participants will gain the ability to:

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

Format of the Course

• Guided presentations and applied technical discussion.
• 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.