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Webinar: Flexible machine learning solutions in the AWS Cloud

Our free webinar shows you how to implement a fully fledged and scalable machine-learning solution with standard AWS services.


How to bring smart data pipelines into the cloud? – A guide involving standard AWS services

In addition to artificial intelligence, machine learning – a generic term for the generation of knowledge from experience – is a major trending topic being addressed by more and more companies. But how are machine-learning processes brought into the cloud so that we can access our data from around the world?

We will show you how: The webinar provides an overview of possibilities of running machine-learning processes in the AWS cloud.

Our speakers will cover the following topics at the webinar:

- Machine learning and smart data pipelines

- Obstacles during realization of production

- Advantages of a flexible AWS cloud solution for machine learning

- AWS services for the smart data pipeline

- Presentation of reference architecture

- Discussion of alternative approaches

Who should participate?

Are you a BI & data-science manager, data architect & engineer or analytics product owner? Then you should definitely not miss this free webinar by our two colleagues Max Kurthen and Ingo Klose.

Take advantage of this opportunity and register here for the webinar on 28th May from 11:00 to 12:00

Please note that the webinar will be held in German.

Our Speakers

Maximilian Kurthen is a consultant for Data Science and Data Engineering. His focus is on the go-live of Data Science projects, especially in the cloud context. He has industry knowledge in the areas of media, mobility and oil & gas, among others.

Ingo Klose is a consultant with a focus on business intelligence, data warehouse, ETL, requirements analysis, project management and cloud-based systems. He advises clients from various industries such as media and telecommunications, trade, Internet and e-commerce on the topics of business intelligence, data warehousing and cloud systems.


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