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Federated Learning in IoT and Edge Computing

 14 Hours
This instructor-led, live training in Hong Kong (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.
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Edge AI for IoT Applications

 14 Hours
This instructor-led, live training in Hong Kong (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.
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Edge Computing

 7 Hours
This instructor-led, live training in Hong Kong (online or onsite) is aimed at product managers and developers who wish to use Edge Computing to decentralize data management for faster performance, leveraging smart devices located on the source network. By the end of this training, participants will be able to:
  • Understand the basic concepts and advantages of Edge Computing.
  • Identify the use cases and examples where Edge Computing can be applied.
  • Design and build Edge Computing solutions for faster data processing and reduced operational costs.
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