Online Internet of Things (IoT) Training in India

This instructor-led, live training in India (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 India (online or onsite) is designed for 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 core 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 capabilities in edge computing environments.
• Tackle challenges associated with data privacy and network limitations in IoT systems.

This instructor-led, live training in India (online or onsite) is tailored for intermediate-level developers, system architects, and industry professionals who wish to leverage Edge AI to enhance IoT applications with intelligent data processing and analytics capabilities.

By the conclusion of this training, participants will be equipped 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 India (online or onsite) targets intermediate-level IoT developers, embedded engineers, and AI professionals who want to implement TinyML for predictive maintenance, anomaly detection, and smart sensor applications.

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

• Grasp the fundamentals of TinyML and its applications in IoT.
• Set up 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 in India (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.

The primary goal of this training is to elucidate the nature of 5G networks and their profound impact on smart technologies. We aim to present a balanced view of the advantages and disadvantages inherent in the synergy between 5G and IoT, while outlining the developmental trajectory of a network infrastructure originally designed for the smart world.

In this instructor-led live training conducted at India, participants will learn how to maximise Nginx performance by setting up, configuring, monitoring, and troubleshooting it for various HTTP and TCP traffic scenarios. Key topics include configuring critical Nginx parameters and optimising the OS and virtual machine settings to derive the greatest benefit from Nginx.