Papers › Kafka-ML: connecting the data stream with ML/AI frameworks
Kafka-ML: connecting the data stream with ML/AI frameworks
Cristian Martín, Peter Langendoerfer, Pouya Soltani Zarrin, Manuel Díaz, Bartolomé Rubio
Machine Learning (ML) and Artificial Intelligence (AI) have a dependency on data sources to train, improve and make predictions through their algorithms. With the digital revolution and current paradigms like the Internet of Things, this information is turning from static data into continuous data streams. However, most of the ML/AI frameworks used nowadays are not fully prepared for this revolution. In this paper, we proposed Kafka-ML, an open-source framework that enables the management of TensorFlow ML/AI pipelines through data streams (Apache Kafka). Kafka-ML provides an accessible and user-friendly Web User Interface where users can easily define ML models, to then train, evaluate and deploy them for inference. Kafka-ML itself and its deployed components are fully managed through containerization technologies, which ensure its portability and easy distribution and other features such as fault-tolerance and high availability. Finally, a novel approach has been introduced to manage and reuse data streams, which may lead to the (no) utilization of data storage and file systems.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections