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ROOM E101/102 | Interactive machine learning predictions using microservices and Tableau
Bringing advanced analytics to end users through well-established reporting tools can be challenging. The solution we present in this talk enables business users to directly receive machine learning (ML) predictions for their custom selections in Tableau. We use Big Data and AWS (PySpark) technologies for our ML pipeline. The ML models are then deployed to MLflow and two-way communication to Tableau is ensured through APIs.
Johannes Mellenthin promovierte in Teilchenphysik an der Universität Göttingen und am CERN. Er hat langjährige Erfahrung im Consulting im Bereich Data Science. Neben der technischen Implementierung von End-to-End-Machine-Learning-Lösungen konzentriert er sich auf die Identifizierung von Möglichkeiten für Unternehmen, langfristig den größten Nutzen aus ihren Daten zu ziehen.
Johannes Mellenthin completed his doctorate in Particle Physics at the University of Göttingen and CERN. He has several years of experience in consulting in the Data Science sector. Besides the technical implementation of end-to-end Machine Learning solutions, he focuses on identifying opportunities for businesses to get the most value out of their data.
