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Federated Learning in IoT and Edge Computing

 14 Hours
This instructor-led, live training in Finland (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 Finland (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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Digital Transformation with IoT and Edge Computing

 14 Hours
This instructor-led, live training in Finland (online or onsite) is aimed at 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.
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Advanced Edge Computing

 21 Hours
Delve deeper into the innovative realm of edge computing with this advanced course. Explore sophisticated architectures and tackle integration challenges, preparing to leverage the full potential of edge computing in a variety of business environments. Gain expertise in cutting-edge tools and methodologies to deploy, manage, and optimize edge computing solutions that meet specific industry needs.
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Applied Edge AI

 35 Hours
Combine the transformative power of AI with the agility of edge computing in this comprehensive course. Learn to deploy AI models directly on edge devices, from understanding CNN architectures to mastering knowledge distillation and federated learning. This hands-on training will equip you with the skills to optimize AI performance for real-time processing and decision-making at the edge.
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Edge Computing

 7 Hours
This instructor-led, live training in Finland (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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Edge Computing Infrastructure

 28 Hours
Build a strong foundation in designing and managing a resilient edge computing infrastructure. Learn about open hybrid cloud infrastructures, managing workloads across diverse clouds, and ensuring flexibility and redundancy. This training provides essential knowledge on creating a scalable and secure infrastructure that supports the dynamic needs of modern applications with edge computing.
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