How to Apply Machine Learning to Real Time Processing

This is a past event

65 people went


Update: Quite a few people are planning to come, so I organized a room at the OHM Hochschule again.

Parking will be possible between our faculty buildings (check on Google maps) or on the parkdeck on the south side of the HQ building (barrier will be open). Entrance to both is from Deichslerstr.

See you next Tuesday.

Regards, Jens


Hello together,

please save the date for our first Big Data Meetup in 2017. Our guest speaker in January is Kai Waehner from Tibco. See the details on the talk and the speaker below.

For now, the Big Data Lab e.V. wishes you a Merry Christmas and a peaceful new year.

See you in January, Jens


About the Talk:

Big Data is key for innovation in many industries today. Large amounts of historical data are stored and analyzed in Hadoop, Spark or other clusters to find patterns and insights, e.g. for predictive maintenance or cross-selling. However: How do you increase revenue or reduce risks in new transactions proactively by applying these insights?

Stream processing is the solution to embed patterns into future actions in real-time. This session discusses and demos how analytic models are built with machine learning frameworks such as R, Apache Spark MLlib,, and how these models are integrated into real-time event processing frameworks.

About the Speaker:

Kai Wähner works as Technology Evangelist at TIBCO. Kai’s main area of expertise lies within the fields of Big Data, Advanced Analytics, Machine Learning, Integration, Microservices, Internet of Things and Blockchain. He is regular speaker at international IT conferences such as JavaOne, O’Reilly Software Architecture or ApacheCon, writes articles for professional journals, and shares his experiences with new technologies on his blog ( ). Contact and references: [masked] / @KaiWaehner / (