Internet of Things (IoT) Training in Argentina

This instructor-led, live training in Argentina (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.

Delivered by an instructor via live online or onsite sessions in Argentina, this training targets intermediate-level professionals who wish 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.
• Deploy Federated Learning models on IoT devices for decentralized AI processing.
• Minimize latency and enhance real-time decision-making within edge computing environments.
• Tackle challenges concerning data privacy and network limitations in IoT systems.

This instructor-led, live training in Argentina (online or onsite) targets intermediate-level developers, system architects, and industry professionals aiming to leverage Edge AI to enhance IoT applications with intelligent data processing and analytics capabilities.

By the conclusion 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 Argentina (online or onsite) is designed for intermediate-level IoT developers, embedded engineers, and AI practitioners who want 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 IoT applications.
• Configure 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 Argentina (online or onsite) is tailored for intermediate-level IT professionals and business managers who want to understand how IoT and edge computing can drive efficiency, real-time processing, and innovation across diverse industries.

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

• Comprehend the foundational principles of IoT and edge computing and their significance in digital transformation.
• Identify practical use cases for IoT and edge computing in manufacturing, logistics, and energy.
• Differentiate between edge and cloud computing architectures along with their deployment scenarios.
• Deploy edge computing solutions for predictive maintenance and real-time decision-making.

This training aims to clarify what a 5G network is and its impact on smart technologies. We will explore both the advantages and disadvantages of the synergy between these technologies (5G/IoT) and highlight the development paths of a network designed from the ground up for the smart world.

In this instructor-led live training held in Argentina, participants will learn how to maximize Nginx's performance while setting up, configuring, monitoring, and troubleshooting it to manage various forms of HTTP and TCP traffic. Key topics include configuring the most important parameters in Nginx, the OS, and virtual machines to gain maximum value from the platform.