TinyML for IoT Applications Training Course

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 intermediate-level IT professionals and business managers who wish to understand the potential of IoT and edge computing for enabling efficiency, real-time processing, and innovation in various industries.

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

• Understand the principles of IoT and edge computing and their role in digital transformation.
• Identify use cases for IoT and edge computing in manufacturing, logistics, and energy sectors.
• Differentiate between edge and cloud computing architectures and deployment scenarios.
• Implement edge computing solutions for predictive maintenance and real-time decision-making.

This instructor-led, live training in South Korea (online or onsite) is aimed at intermediate-level developers, system architects, and industry professionals who wish to leverage Edge AI for enhancing IoT applications with intelligent data processing and analytics capabilities.

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

• Understand the fundamentals of Edge AI and its application in IoT.
• Set up and configure Edge AI environments for IoT devices.
• Develop and deploy AI models on edge devices for IoT applications.
• Implement real-time data processing and decision-making in IoT systems.
• Integrate Edge AI with various IoT protocols and platforms.
• Address ethical considerations and best practices in Edge AI for IoT.

This instructor-led, live training in South Korea (online or onsite) targets product managers and developers who seek to utilize Edge Computing for decentralized data management, thereby enhancing performance through smart devices located at the network source.

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

• Grasp the fundamental concepts and benefits associated with Edge Computing.
• Recognize specific use cases and practical examples applicable to Edge Computing.
• Design and implement Edge Computing solutions that enable accelerated data processing and lower operational expenses.

This instructor-led, live training in South Korea (online or onsite) is designed for intermediate-level professionals seeking to apply Federated Learning to optimize IoT and edge computing solutions.

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

• Grasp the core principles and advantages of Federated Learning within IoT and edge computing contexts.
• Deploy Federated Learning models on IoT devices to enable decentralized AI processing.
• Minimize latency and enhance real-time decision-making capabilities in edge computing environments.
• Address challenges related to data privacy and network constraints in IoT systems.

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.

In this instructor-led live training in South Korea, participants will explore the various aspects of NB-IoT (also known as LTE Cat NB1) while developing and deploying a sample NB-IoT-based application.

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

• Identify the different components of NB-IoT and how they fit together to form an ecosystem.
• Understand and explain the security features built into NB-IoT devices.
• Develop a simple application to track NB-IoT devices.

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.

ThingsBoard is an open-source IoT platform that provides device management, data collection, processing, and visualization capabilities for your IoT solutions.

In this instructor-led live training, participants will learn how to integrate ThingsBoard into their IoT architectures.

Upon completing this training, participants will be able to:

• Install and configure ThingsBoard
• Grasp the core concepts of ThingsBoard features and architecture
• Develop IoT applications using ThingsBoard
• Integrate ThingsBoard with Kafka to route telemetry data from devices
• Integrate ThingsBoard with Apache Spark to aggregate data from multiple devices

Audience

• Software engineers
• Hardware engineers
• Developers

Course Format

• A blend of lectures, discussions, exercises, and extensive hands-on practice

Note

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

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.

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.

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.

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.