Operationalized Data Science for Production Optimization: Saving Costs at BMW Group | Virtual Event

Proactively identifying areas to improve the production process can be difficult, but by using data science models with Splunk you can derive deep insights from operations and optimize the production process. The BMW Group has operationalized this strategy with Splunk as one of the core elements of their production systems to collect and analyze a variety of data along the car manufacturing process. The data is used to save costs, maximize productivity, and increase product quality.

In this webinar, we will share how Splunk Enterprise, and the Splunk App for Data Science and Deep Learning (DSDL) are used to enable cutting edge research and accelerate the time to operationalize models in production quickly.

We will discuss how you can:

Use the DSDL app for predictive quality use cases in manufacturing.
Operationalize workflows for leveraging data science with Splunk and DSDL.
Integrate data science models directly with Splunk Classic Dashboard and Dashboard Studio.
Use GPU computing for better performance with machine learning.

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Operationalized Data Science for Production Optimization

Saving Costs at BMW Group

On-Demand

Operationalized Data Science for Production Optimization:

Our Speakers

Philipp Drieger

Andreas Schoch