Online Internet of Things (IoT) Training in the Netherlands

This instructor-led, live training in the Netherlands (online or onsite) is designed for advanced-level IoT developers and smart home enthusiasts who want to automate IoT processes and create innovative solutions using n8n.

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

• Set up and configure n8n for IoT workflow automation.
• Integrate IoT devices and platforms using n8n nodes and connectors.
• Implement custom workflows to automate IoT tasks and processes.
• Utilize IoT protocols such as MQTT and REST APIs within n8n workflows.
• Monitor, troubleshoot, and optimize IoT automation workflows.

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

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

• Understand the principles and benefits of Federated Learning in IoT and edge computing.
• Implement Federated Learning models on IoT devices for decentralized AI processing.
• Reduce latency and improve real-time decision-making in edge computing environments.
• Address challenges related to data privacy and network constraints in IoT systems.

This instructor-led, live training in the Netherlands (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 the Netherlands (online or onsite) is designed for intermediate-level IoT developers, embedded engineers, and AI practitioners who wish to implement TinyML for predictive maintenance, anomaly detection, and smart sensor applications.

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

• Understand 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.

This instructor-led live training in the Netherlands (online or onsite) is targeted at 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 across various industries.

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

• Grasping the core principles of IoT and edge computing and their significance in digital transformation.
• Recognizing use cases for IoT and edge computing within the manufacturing, logistics, and energy sectors.
• Differentiating between edge and cloud computing architectures and deployment models.
• Deploying edge computing solutions to facilitate predictive maintenance and real-time decision-making.

This training aims to clarify the nature of 5G networks and their influence on smart technologies. We will explore both the benefits and drawbacks of the synergy between 5G and IoT, as well as the developmental trajectory of a network designed from its inception for the intelligent world.

In this instructor-led live training the Netherlands, participants will learn how to maximize Nginx's performance by setting it up, configuring it, monitoring it, and troubleshooting it to handle various types of HTTP and TCP traffic. Key topics include configuring the most critical parameters in Nginx, the operating system, and virtual machines to extract maximum value from the platform.