Talk, Art of Comppression @Bitzer, Sindelfingen
In this talk I give some insights on how we use Artificial Intelligence in order to make a digital product of a mechancial engineering company really intelligent. It show the whole pathway from sensor data to an Artificial Intelligence Service like anomaly detection or Predictive Maintenance.
Talk, Embedded World Conference, Nürnberg
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.
Talk, Machine Learning Week Europe, München
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.
Talk, Minds Mastering Machines, Köln
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.
Talk, Praxistag VDMA: Künstliche Intelligenz im Maschinen- und Anlagenbau, Frankfurt
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.
Talk, 46. Deutsche Compoundiertagung 2022, Nürnberg
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.
Talk, Digital Pioneers, Virtual
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.
Podcast, KI in der Industrie, Virtual
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.
Talk, Predictive Analytics World for Industry 4.0, Virtual Conference
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.
Talk, AIxIA – Artificial Intelligence meets Intelligence Artificielle, Karlsruhe, Germany
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.