Online or onsite, instructor-led live Edge Computing training courses demonstrate through interactive hands-on practice how to use Edge Computing to decentralize data management for faster performance, leveraging smart devices located on the source network.
Edge Computing training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live Edge Computing training can be carried out locally on customer premises in Trojmiasto or in NobleProg corporate training centers in Trojmiasto.
NobleProg -- Your Local Training Provider
Gdynia
Hotel Nadmorski, Ejsmonda 2, Gdynia, Poland, 81-409
The training room is located just 3 kilometers from the PKP/PKS Station in Gdynia, making it easily accessible for participants traveling by train or bus. Additionally, it is only 400 meters away from the bus stop, facilitating access even for those using public transportation. It is equipped with necessary training tools such as a projector, screen, and flipchart, providing comfortable conditions for both participants and the trainer.
Gdańsk
Hotel Fahrenheit, Grodzka 19, Gdańsk, Poland, 80-841
The training room is located in the very heart of the picturesque Gdansk Old Town, making the surroundings not only inspiring but also exceptionally attractive for participants. Within close proximity, you can find the railway and bus stations, facilitating arrival for those traveling by both train and bus. Additionally, the airport and port are also within reach, making this location convenient for individuals coming from distant places, both domestically and internationally.
This instructor-led, live training in Trojmiasto (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.
This instructor-led, live training in Trojmiasto (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 Trojmiasto (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.
This instructor-led, live training in Trojmiasto (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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