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Posts
Data Platform - the centerpiece where it all comes together - Part 2
Published:
In my preceded blogpost I introduced into the basics of a modern Data Platform. In this blogpost I continue (part 2) describing the components of a data platform as well as its functionalities.
Data Platform - the centerpiece where it all comes together - Part 1
Published:
A data platform serves as the “link” between IoT connectivity and AI services. It enables the seamless integration and consolidation of data from various sources while acting as a robust foundation for AI services. Additionally, a data platform can provide data reliability, scalability, and robustness while also considering security, governance, and cost constraints. In this Blogpost I will describe some basics of a dataplatform and the requirements a dataplatform should fulfill. In the following blogpost (part 2) I will describe the components of a data platform as well as its functionalities.
Connectivity - the basis for local and global data exchange - Part 2
Published:
In the first part of this blogpost series, fundamental and strategical questions about connectivity have been adressed. This second part delves into the operationalization of hardware components and communication protocols essential for cloud connectivity.
Connectivity - the basis for local and global data exchange - Part 1
Published:
WHY move the local machine world into the cloud at all? Is local connectivity on the machine side not sufficient? If I want to bring my local machine world into the cloud, which data integration strategy is the right one for me? These are precisely the questions I address within this blogpost and the underlying whitepaper.
How to get from Sensor Data to a digital product
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“We should digitalise our product! Digital twin, predictive maintenance, intelligent quality control: can’t we offer that too?” Service managers from the mechanical engineering sector are increasingly confronted with questions like these. Do such questions sound familiar to you? This blogpost together with follow up blogposts as well as the underlying whitepaper provides the technical and strategic answers as well as a fact-based introduction to the world of product digitalisation.
Using AWS Lambda with custom container image for Machine Learning inference
Published:
Using serverless offerings from cloud providers has many advantages like no provisioning and managing of servers and automatic scaling. But serverless functions also come with some limitiations like complicated deployment package management, limited package size and limited RAM. Especially if you want to use lambda functions for Machine Learning model inference, these limitations can be quite restrictive. AWS also recognized that so since December 2020 they extended their lambda offering by container image support. In this post I’ll describe my experiences using aws lambda with custom container images and describe the ups and downs of this approach.
IoT platform switching: From Conrad Connect to iobroker
Published:
In our research project Smart Air we were forced to get rid of Conrad Connect IoT platform. Why? To our annoyance they just shot it down - and announced the shut down just one month in advance! In this post I’ll describe what had to be done to migrate to the open source IoT platform iobroker and why I’m actually happy about this forced IoT platform switching.
portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
Short description of portfolio item number 2
publications
Paper Title Number 1
Published in Journal 1, 2009
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1). http://academicpages.github.io/files/paper1.pdf
Paper Title Number 2
Published in Journal 1, 2010
This paper is about the number 2. The number 3 is left for future work.
Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2). http://academicpages.github.io/files/paper2.pdf
Paper Title Number 3
Published in Journal 1, 2015
This paper is about the number 3. The number 4 is left for future work.
Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3). http://academicpages.github.io/files/paper3.pdf
talks
How Data Science was used to optimize energy consumption of transalpine oil pipeline
Published:
In this talk, I give some insights about a Data Science project I did at TAL Group. TAG Group is a pipeline operator that transports crude oil from the Mediteranean Sea all over the alps to different rafineries in Austria, Germany and Czech Republic. Therefore many pumps are operating all along the pipeline and in this project we identified inefficies and its root causes using Machine Learning.
Making Sense out of Sensor Data - Data Science at TAL-Group
Published:
In this talk, I give some insights about a Data Science project I did at TAL Group. TAG Group is a pipeline operator that transports crude oil from the Mediteranean Sea all over the alps to different rafineries in Austria, Germany and Czech Republic. Therefore many pumps are operating all along the pipeline and in this project we identified inefficies and its root causes using Machine Learning.
Kurz KI an der Pipeline
Published:
In this podcast, I give some insights about a Data Science project I did at TAL Group. TAG Group is a pipeline operator that transports crude oil from the Mediteranean Sea all over the alps to different rafineries in Austria, Germany and Czech Republic. Therefore many pumps are operating all along the pipeline and in this project we identified inefficies and its root causes using Machine Learning.
Produktdigitalisierung in der Industrie - Wann sollte ich eigentlich an Data Analytics & AI Denken?
Published:
In this talk, I describe my view on typical problems that occure during product digitalisation in industry companies. Especially, I give my optinion on on the “right” point in time to think about Data Analytics and Artificial Intelligence during the product digitalization cycle.
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Data Science - Der Weg vom Sensor in die Cloud bis hin zur Endanwendung
Published:
In this talk at VDI conference, I describe different views on Data and Data Science depending if you are a plant operators or a plant manufacturer. Furthermore, I give some insights about IT Infrastructure that brings sensor data from machines into the cloud in order to provide data driven services. Finally, I show my view on how Data Science is evolving in copounding technologies.
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Anomalieerkennung bei der KSB: Vom Experiment zum Produktiveinsatz von Maschine Learning Modellen
Published:
In this talk at VDMA Praxistag, I describe the different steps that are necessary to get from experimental phase of a digital Product to productive use of machine learning models. The talk is based on my project experiences at our customer KSB, that introduced a Digital Product for its pumps.
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Praxisbericht: ML Ops für Condition-Monitoring Anwendungen
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In this talk I worked out some talkeaways regarding MLOPS from one of our customer projects: Firstly, a clear understanding of the importance of MLOps for the long-term operation of machine learning models. Secondly, I gave Insight into which problems AWS Sagemaker can and cannot solve (model registry, multi-model endpoints, serverless endpoints,…). Finally, I conveyed an understanding of a service architecture that promotes seamless collaboration between data science and operations teams.
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How MLOps Practices Speed Up the Development of Condition Monitoring Services at KSB SE & Co. KgaA
Published:
At KSB SE & Co. KGaA, the number of productive ML-models increased with the growing customer base of its digital product KSB Guard. As a result, the need for a clear separation of responsibilities between data scientists and operations increased. In particular, the further development of already productive models became a pain point. In this talk I presented a service architecture that promotes seamless collaboration between data science and operations teams.
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How to get from Sensor Data to an AI based Realtime Application - A technical Deep Dive
Published:
Which components does it need to create a predictive Maintenance application based on vibration data created by an industrial machine? That is a question that I get asked from many manufacturing companies that think about extending their traditional business by digital solutions. In this talk I try to answer that question leading through a showcase project and explaining each commponent of the project.
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.