Internet of Things (IoT) Training in Thessaloniki

This instructor-led, live training in Thessaloniki (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 Thessaloniki (available online or on-site) 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 principles and advantages of Federated Learning in 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.
• Overcome challenges related to data privacy and network limitations in IoT systems.

This instructor-led, live training in Thessaloniki (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 Thessaloniki (online or onsite) is designed for 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 practical applications within IoT.
• Establish a TinyML development environment tailored for IoT projects.
• Create and deploy ML models onto low-power microcontrollers.
• Utilize TinyML to implement predictive maintenance and anomaly detection solutions.
• Optimize TinyML models to ensure efficient power consumption and memory utilization.

This instructor-led, live training in Thessaloniki (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 training aims to clarify the nature of 5G networks and their influence on smart technologies. We will examine both the benefits and drawbacks of the synergy between 5G and IoT, while also outlining the developmental trajectory of a network designed from the outset for the smart ecosystem.

Delve deeper into the innovative realm of edge computing with this advanced course. Explore sophisticated architectures and tackle integration challenges, preparing to leverage the full potential of edge computing in a variety of business environments. Gain expertise in cutting-edge tools and methodologies to deploy, manage, and optimize edge computing solutions that meet specific industry needs.

Combine the transformative power of AI with the agility of edge computing in this comprehensive course. Learn to deploy AI models directly on edge devices, from understanding CNN architectures to mastering knowledge distillation and federated learning. This hands-on training will equip you with the skills to optimize AI performance for real-time processing and decision-making at the edge.

Establish a solid foundation in designing and managing a resilient edge computing infrastructure. Gain insights into open hybrid cloud systems, workload management across diverse clouds, and the importance of flexibility and redundancy. This course provides essential knowledge for building scalable and secure infrastructure that supports the dynamic needs of modern applications through edge computing.

In this instructor-led live training conducted in Thessaloniki, participants will learn how to optimize Nginx's performance by setting up, configuring, monitoring, and troubleshooting it for various forms of HTTP and TCP traffic. The curriculum covers configuring essential Nginx parameters, alongside the operating system and virtual machine settings, to fully leverage Nginx's capabilities.