As data-driven solutions based on machine and deep learning are gaining more and more momentum, workflows to build and deploy such services in a reliable and flexible fashion are of utmost importance. The BMW Group IT team drives the usage of data-driven technologies and forms the nucleus of a data-centric culture inside of the organization. Part of the team’s mission is the establishment of a globally available and scalable data lake equipped with data science tools that help efficiently process and analyze data.
Tobias Bürger and Josef Viehhauser detail the path the BMW Group has taken and the established technology stack and explore the challenges faced along the way. Tobias and Josef also offer an overview of novel machine learning use cases, such as those based on gradient boosting and convolutional neural nets, that have been applied and deployed in real-world environments. Along the way, you’ll learn the value that has been created for domains including but not limited to connected vehicles, vehicle development, and aftersales.
Josef Viehhauser is a full stack data scientist at the BMW Group, where he leverages machine learning to create data-driven applications and improve established workflows along the company’s value chain. Josef also works on scoping and implementing such use cases in scalable ecosystems primarily via Python. Outside of work, he is interested in technological innovations and soccer.
Tobias Bürger leads the Platform and Architecture Group within the Big Data, Machine Learning, and Artificial Intelligence Department at BMW Group, where he is responsible for the global big data platform that is the core technical pillar of the BMW data lake and is used across different divisions inside the BMW Group, spanning areas such as production, aftersales, and ConnectedDrive.
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