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Why machine learning should take place directly on the end device

IoT - Everything in the Cloud?

29 April 2021

This webinar will use examples to explain how IoT projects are usually structured and what challenges there are in transferring the data to the IoT cloud. There are different reasons why pre-processing the collected data at the end device - i.e., at the "edge device" - makes sense; these can be, for example, the connection, the amount or the nature of the data. Thanks to Edge ML, the complex processing of information can now take place efficiently at the end device.

Webinar

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Stefanie Pichler

As Solution Manager at A1 Digital, Stefanie Pichler is responsible for the solution portfolio around Machine Learning and Advanced Analytics. During her several years in solution management, the mathematics graduate also managed and supported agile development projects and, as an interface between the market and product development, is always involved with a broad spectrum of solutions and technologies in the very dynamic environment of IoT and machine learning.

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Martin Sailer

Martin Sailer works in the Vertical Market Solutions department and, in his role as IoT Consultant, advises companies from various industries on the decision and design of end-to-end IoT solutions. His special focus is the deployment and further development of the solution portfolio for the retail sector. In his several years of experience in Central and Eastern Europe in the telecom industry, he was responsible for solution design, product management and international project delivery of telecom solutions.

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Dieter Mayr

Dieter Mayr is a Senior Data Scientist and Machine Learning Consultant responsible for the development and technical implementation of complex Machine Learning projects. His focus is the data-based conceptual design and support of machine learning projects in close coordination with IT departments and business stakeholders. During his PhD in Optimization and his several years of employment in the energy sector, he was able to build up profound solution expertise and implementation experience for complex and innovative Machine Learning and Data Analytics topics.