Internet of Things (IoT) Training in Aguascalientes

This instructor-led, live training in Aguascalientes (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 Aguascalientes (online or onsite) 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 core principles and advantages of Federated Learning in IoT and edge computing.
• 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.
• Address challenges associated with data privacy and network limitations in IoT systems.

This instructor-led, live training in Aguascalientes (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 Aguascalientes (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:

• 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 Aguascalientes (online or onsite) is tailored for intermediate IT professionals and business managers who want to explore the potential of IoT and edge computing to enhance efficiency, enable real-time processing, and foster 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 what the 5G network is and its influence on smart technologies. It explores both the benefits and drawbacks of the 5G/IoT technological synergy, while highlighting the development trajectory of a network designed from the outset for the smart ecosystem.

In this instructor-led live training located in Aguascalientes, participants will learn how to maximize the performance of Nginx by setting up, configuring, monitoring, and troubleshooting it for handling various forms of HTTP and TCP traffic. Topics covered include configuring the most important parameters in Nginx, the OS, and a virtual machine to gain maximum value out of Nginx.