# TensAIR: Real-Time Training of Neural Networks from Data-streams

arXiv: https://arxiv.org/abs/2211.10280 · HTML: https://mlsanity.com/paper/2211.10280/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 44 references that are in the map.
- First posted on arXiv 18 Nov 2022.

## Builds on (most cited first, 11 of 11)

- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Horovod: fast and easy distributed deep learning in TensorFlow](https://mlsanity.com/paper/1802.05799/) (2018)
- [Online Learning: A Comprehensive Survey](https://mlsanity.com/paper/1802.02871/) (2018)
- [Asynchronous Decentralized Parallel Stochastic Gradient Descent](https://mlsanity.com/paper/1710.06952/) (2017)
- [Scalable Deep Learning on Distributed Infrastructures: Challenges, Techniques and Tools](https://mlsanity.com/paper/1903.11314/) (2019)
- [On the Reliable Detection of Concept Drift from Streaming Unlabeled Data](https://mlsanity.com/paper/1704.00023/) (2017)
- [SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication](https://mlsanity.com/paper/2210.14026/) (2022)
- [Asynchronous Coordinate Descent under More Realistic Assumptions](https://mlsanity.com/paper/1705.08494/) (2017)
- [Taming Convergence for Asynchronous Stochastic Gradient Descent with Unbounded Delay in Non-Convex Learning](https://mlsanity.com/paper/1805.09470/) (2018)
- [Benchmarking Distributed Stream Data Processing Systems](https://mlsanity.com/paper/1802.08496/) (2018)
- [AIR: A Light-Weight Yet High-Performance Dataflow Engine based on Asynchronous Iterative Routing](https://mlsanity.com/paper/2001.00164/) (2020)

## Built on by (most cited first, 2 of 2)

- [Convergence Analysis of Decentralized ASGD](https://mlsanity.com/paper/2309.03754/) (2023)
- [AI-Driven HSI: Multimodality, Fusion, Challenges, and the Deep Learning Revolution](https://mlsanity.com/paper/2502.06894/) (2025)

## Abstract (arXiv)

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.