Internet of Things (IoT) Training in Portugal

This instructor-led, live training in Portugal (online or onsite) is designed for advanced IoT developers and smart home enthusiasts looking to automate IoT processes and develop 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.
• Utilise IoT protocols such as MQTT and REST APIs within n8n workflows.
• Monitor, troubleshoot, and optimise IoT automation workflows.

This instructor-led, live training in Portugal (online or onsite) is aimed at intermediate-level professionals who wish 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 Portugal (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 Portugal (online or onsite) is aimed at 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:

• Grasp the fundamentals of TinyML and its applications in IoT.
• Configure a TinyML development environment for IoT projects.
• Create 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 session in Portugal (online or onsite) is designed for intermediate IT professionals and business managers who wish to explore the potential of IoT and edge computing to drive 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.
• Recognising practical applications of IoT and edge computing within manufacturing, logistics, and energy sectors.
• Distinguishing between edge and cloud computing architectures as well as their respective deployment scenarios.
• Deploying edge computing solutions to support predictive maintenance and real-time decision-making.

This training aims to clarify what the 5G network entails and its influence on smart technologies. We will explore both the benefits and drawbacks of the interplay between 5G and IoT, while also outlining the developmental trajectory of a network designed from the outset for the smart world.

In this instructor-led live training held in Portugal, participants will learn how to maximise Nginx's performance as they set up, configure, monitor, and troubleshoot Nginx for handling various forms of HTTP and TCP traffic. The curriculum covers how to configure the most essential parameters in Nginx, the operating system, and a virtual machine to gain maximum value from Nginx.