TinyML for IoT Applications Training Course

This instructor-led, live training in Taiwan (online or onsite) targets intermediate-level telecom professionals, AI engineers, and IoT specialists interested in exploring how 5G networks accelerate Edge AI applications.

Upon completing this training, participants will be able to:

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

6G and the Intelligent Edge is a forward-looking course that explores the integration of 6G wireless technologies with edge computing, IoT ecosystems, and AI-driven data processing to support intelligent, low-latency, and adaptive infrastructures.

This instructor-led, live training (online or onsite) is aimed at intermediate-level IT architects who wish to understand and design next-generation distributed architectures leveraging the synergy of 6G connectivity and intelligent edge systems.

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

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

Format of the Course

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

Course Customization Options

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

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

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

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

• Collect, prepare, and manage datasets specifically for TinyML applications.
• Train and optimize models for low-power microcontrollers.
• Convert models into lightweight formats suitable for edge devices.
• Deploy, test, and monitor TinyML applications in real-world hardware environments.

Course Format

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

Course 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 Taiwan (online or onsite) is designed for intermediate-level IT professionals and business managers who aim to understand the potential of IoT and edge computing for driving efficiency, real-time processing, and innovation across various industries.

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

• Grasp the foundational concepts of IoT and edge computing and their significance in digital transformation.
• Identify practical use cases for IoT and edge computing in manufacturing, logistics, and energy.
• Differentiate between edge and cloud computing architectures and their deployment contexts.
• Implement edge computing solutions for predictive maintenance and real-time decision-making.

This instructor-led, live training in Taiwan (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:

• Grasp the fundamentals of Edge AI and its application in IoT.
• Establish and configure Edge AI environments for IoT devices.
• Create 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 Taiwan (online or onsite) targets intermediate-level professionals who wish to apply Federated Learning to optimize IoT and edge computing solutions.

Upon completing this training, participants will be able to:

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

This instructor-led, live training session at Taiwan (online or onsite) targets 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 fundamentals of TinyML and its advantages for edge AI applications.
• Configure 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 power efficiency and address memory constraints.

TinyML refers to the deployment of machine learning models on hardware with severe resource constraints.

This instructor-led, live training (available online or onsite) targets advanced practitioners aiming 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 compromising accuracy.
• Benchmark TinyML models for latency, memory usage, and energy efficiency.
• Implement optimized inference pipelines on microcontrollers and edge devices.
• Evaluate the trade-offs between performance, accuracy, and hardware limitations.

Course Format

• 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 is an approach to deploying machine learning models on low-power, resource-constrained devices operating at the network edge.

This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to secure TinyML pipelines and implement privacy-preserving techniques in edge AI applications.

At the conclusion of this course, participants will be able to:

• Identify security risks unique to on-device TinyML inference.
• Implement privacy-preserving mechanisms for edge AI deployments.
• Harden TinyML models and embedded systems against adversarial threats.
• Apply 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 Taiwan (online or on-site) is designed for novice engineers and data scientists eager to grasp the basics of TinyML, explore its potential uses, and deploy AI models on microcontrollers.

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

• Grasp the core concepts of TinyML and appreciate its importance.
• Deploy efficient AI models on microcontrollers and edge devices.
• Optimize and refine machine learning models to minimize power usage.
• Implement TinyML in practical scenarios like gesture recognition, anomaly detection, and audio processing.

TinyML is a framework for deploying machine learning models on low-power microcontrollers and embedded platforms used in robotics and autonomous systems.

This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to integrate TinyML-based perception and decision-making capabilities into autonomous robots, drones, and intelligent control systems.

Upon finishing this course, participants will be able to:

• Design optimized TinyML models 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.

This instructor-led, live training in Taiwan (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 the integration of machine learning into low-power, resource-constrained wearable and medical devices.

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

Upon completing this training, participants will be able 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 for low-power and memory-constrained wearable devices.
• Evaluate the clinical relevance, reliability, and safety of TinyML-driven outputs.

Course Format

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

Customization Options

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

TinyML refers to a machine learning methodology designed for devices with limited resources and small form factors.

This instructor-led live training (available online or onsite) is designed for beginners to intermediate learners who aim to create functional TinyML applications using Raspberry Pi, Arduino, and comparable microcontrollers.

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

• Gather and preprocess data for TinyML initiatives.
• Train and optimize compact machine learning models suitable for microcontroller environments.
• Deploy TinyML models on Raspberry Pi, Arduino, and similar boards.
• Develop complete embedded AI prototypes from start to finish.

Course Format

• Practical exercises and hands-on experimentation.
• Live-lab projects using actual hardware.

Course Customization Options

• For customized training that aligns with your specific hardware or use case, please contact us to arrange.

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

This instructor-led, live training (available 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.