The implementation of an online video authentication service involves a large variety of different kind of algorithms, ranging from classic decision making models over neural networks and computer vision techniques. To reach a decision as fast as possible, these algorithms need to be parallelized to the full extent and executed on both CPU and GPU cores. At the same time, large amount of video data have to be shared efficiently between tasks. Considering all requirements, Ray, and especially Ray Serve, provide an optimal solution with their platform. In this talk, Tanja Bayer shares reasons to choose Ray Serve over other platforms like Kafka Streams or PySpark. She will describe the process of implementing Widas Technologie Services GmbH's service with Ray and will take a closer look on how they tackled some of the challenges, like synchronizing the output of their models to combine their feedback into one final decision.
Tanja Bayer is a machine learning engineer at Widas Technologie Services GmbH, where she and her colleagues work on a fully automated video identification service. After obtaining her Master degree in Industrial Engineering and Management at Karlsruhe Institut of Technology, she focused on machine learning and is working in this field since two years.