6G and IoT Training Course

This training aims to elucidate the nature of 5G networks and their influence on smart technologies. We will present a balanced view of the 5G and IoT relationship, covering both benefits and drawbacks, while highlighting the network's development path, which was originally designed with the smart world in mind.

This 21-hour training in Thailand empowers IT architects to design next-generation distributed systems. It explores the integration of 6G, edge computing, and AI to build low-latency, scalable infrastructures. Participants will gain practical skills in creating secure, resilient, and intelligent edge architectures to address future business needs.

This live, instructor-led training in Thailand empowers intermediate telecom engineers to transition 4G/5G infrastructure towards 6G standards. The curriculum explores cloud-native RAN, edge computing, and AI-driven operations through interactive labs and design workshops, facilitating the development of actionable migration strategies.

This live, instructor-led training, available online or onsite, is designed for executive-level participants seeking to comprehend the global 6G landscape, evaluate its commercial potential, and formulate early strategic investment plans.

Upon completion, participants will possess the insights necessary to:

• Recognize emerging market trends and global initiatives that are defining the 6G ecosystem.
• Grasp the regulatory and spectrum allocation timelines associated with IMT-2030.
• Assess the shifting vendor landscape and current technology readiness levels.
• Create a roadmap for early-stage investment, research collaborations, and pilot programs.

This advanced, instructor-led course in Thailand examines the convergence of digital twin technology and AI to model and operate next-generation 6G infrastructure. Learners will develop twin models, implement AI for closed-loop optimization, and integrate real-time telemetry to achieve intent-based network control.

Tailored for intermediate-level IT professionals and business managers, this instructor-led live training in Thailand (online or onsite) focuses on understanding how IoT and edge computing can enhance efficiency, enable real-time processing, and drive innovation across diverse industries.

By the conclusion of this training, participants will possess the ability to:

• Grasp the fundamental principles of IoT and edge computing and their contribution to digital transformation.
• Identify relevant use cases for IoT and edge computing in the manufacturing, logistics, and energy industries.
• Differentiate between edge and cloud computing architectures and suitable deployment scenarios.
• Implement edge computing strategies to support predictive maintenance and real-time decision-making.

Delivered as an instructor-led, live training in Thailand (either online or on-site), this course is designed for intermediate-level developers, system architects, and industry professionals aiming to empower their IoT applications with intelligent data processing and analytics through Edge AI.

By the conclusion of this training, participants will have acquired the ability to:

• Comprehend the fundamentals of Edge AI and its role 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.

Delivered by an instructor in Thailand (either online or onsite), this live training is designed for intermediate-level professionals looking to leverage Federated Learning to enhance their IoT and edge computing solutions.

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

• Comprehend the foundational principles and benefits of Federated Learning in IoT and edge computing.
• Deploy Federated Learning models on IoT devices for decentralized AI operations.
• Decrease latency and strengthen real-time decision-making in edge environments.
• Address data privacy and network constraint challenges inherent to IoT systems.

Discover the technical and strategic pillars of 6G in this accessible course designed for beginners. Delivered in Thailand, the program examines key technologies, spectrum policies, and high-value applications. You will learn to differentiate 6G from 5G and create a readiness roadmap that drives informed strategic decisions within your organization.

This live, instructor-led training is delivered Thailand (either online or on-site) and targets advanced IoT developers and smart home enthusiasts seeking to enhance IoT process automation and build innovative solutions using n8n.

By the end of this program, participants will be equipped to:

• Set up and configure n8n for dedicated IoT workflow automation.
• Integrate IoT devices and platforms using n8n's specialized nodes and connectors.
• Create custom workflows that automate specific IoT tasks and processes.
• Utilize IoT protocols like MQTT and REST APIs within n8n workflow structures.
• Monitor, troubleshoot, and optimize the performance of IoT automation workflows.

During this instructor-led live training held in Thailand, participants will learn to maximize Nginx performance by setting up, configuring, monitoring, and troubleshooting the server for various HTTP and TCP traffic types. The curriculum covers the configuration of essential parameters in Nginx, the operating system, and virtual machines to ensure optimal utilization of Nginx's capabilities.

This live, instructor-led training in Thailand (delivered online or onsite) is tailored for intermediate-level IoT developers, embedded engineers, and AI practitioners aiming to deploy TinyML for predictive maintenance, anomaly detection, and smart sensor applications.

By the end of this program, participants will be capable of:

• Comprehending the fundamentals of TinyML and its IoT applications.
• Configuring a TinyML development environment for IoT projects.
• Building and deploying ML models on low-power microcontrollers.
• Executing predictive maintenance and anomaly detection using TinyML.
• Refining TinyML models for optimal power and memory performance.