The combination of Apache Kafka, tiered storage, and machine learning frameworks such as TensorFlow enables you to build one scalable, reliable, but also simple infrastructure for all machine learning tasks using the Apache Kafka ecosystem and Confluent Platform. This discussion features a predictive maintenance use case within a connected car infrastructure, but the discussed components and architecture are helpful in any industry.
In this episode
Technology Evangelist, Confluent
Kai Waehner is a Technology Evangelist at Confluent. He works with customers across the globe and with internal teams like engineering and marketing. Kai’s main area of expertise lies within the fields of Big Data Analytics, Machine Learning, Hybrid Cloud Architectures, Event Stream Processing and Internet of Things. He is a regular speaker at international conferences such as Devoxx, ApacheCon and Kafka Summit, writes articles for professional journals, and shares his experiences with new technologies on his blog: www.kai-waehner.de. Contact and references: firstname.lastname@example.org / @KaiWaehner / LinkedIn (https://www.linkedin.com/in/megachucky/).
Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.