  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.
