  Online learning (OL) from data streams is an emerging area of research that
encompasses numerous challenges from stream processing, machine learning, and
networking. Stream-processing platforms, such as Apache Kafka and Flink, have
basic extensions for the training of Artificial Neural Networks (ANNs) in a
stream-processing pipeline. However, these extensions were not designed to
train ANNs in real-time, and they suffer from performance and scalability
issues when doing so. This paper presents TensAIR, the first OL system for
training ANNs in real time. TensAIR achieves remarkable performance and
scalability by using a decentralized and asynchronous architecture to train ANN
models (either freshly initialized or pre-trained) via DASGD (decentralized and
asynchronous stochastic gradient descent). We empirically demonstrate that
TensAIR achieves a nearly linear scale-out performance in terms of (1) the
number of worker nodes deployed in the network, and (2) the throughput at which
the data batches arrive at the dataflow operators. We depict the versatility of
TensAIR by investigating both sparse (word embedding) and dense (image
classification) use cases, for which TensAIR achieved from 6 to 116 times
higher sustainable throughput rates than state-of-the-art systems for training
ANN in a stream-processing pipeline.
