Internet of Things (IoT) Training in Brno

This instructor-led, live training in Brno (online or onsite) is aimed at advanced-level IoT developers and smart home enthusiasts who wish 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.
• Use IoT protocols like MQTT and REST APIs within n8n workflows.
• Monitor, troubleshoot, and optimize IoT automation workflows.

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

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

• Grasp the fundamental principles and advantages of Federated Learning in IoT and edge computing contexts.
• Deploy Federated Learning models on IoT devices to facilitate decentralized AI processing.
• Minimize latency and enhance real-time decision-making capabilities in edge computing environments.
• Overcome challenges associated with data privacy and network constraints in IoT systems.

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

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

• Grasp the core concepts of TinyML and its role in IoT.
• Establish a TinyML development environment for IoT initiatives.
• Create and deploy machine learning models on low-power microcontrollers.
• Apply TinyML for predictive maintenance and anomaly detection.
• Optimize TinyML models to maximize power efficiency and minimize memory usage.

This instructor-led, live training session in Brno (online or onsite) targets intermediate-level IT professionals and business managers who wish to understand how IoT and edge computing can drive efficiency, real-time processing, and innovation across various industries.

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

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

This training aims to clarify what a 5G network is and its influence on smart technologies. We will highlight both the advantages and limitations of the relationship between 5G and IoT, while exploring the development paths of a network designed from the outset for the smart world.

In this instructor-led live training in Brno, participants will learn how to maximize the performance of Nginx while setting it up, configuring it, monitoring it, and troubleshooting it to handle various forms of HTTP and TCP traffic. Covered topics include how to configure the most important parameters in Nginx, the operating system, and a virtual machine to gain maximum value from Nginx.