mlsanityData to 7 Oct 2026

Apache Submarine: A Unified Machine Learning Platform Made Simple

arXiv 2108.09615 ↗First posted 22 Aug 2021cs.DC cs.AIcited by 1

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Abstract (arXiv)

As machine learning is applied more widely, it is necessary to have a machine learning platform for both infrastructure administrators and users including expert data scientists and citizen data scientists to improve their productivity. However, existing machine learning platforms are ill-equipped to address the "Machine Learning tech debts" such as glue code, reproducibility, and portability. Furthermore, existing platforms only take expert data scientists into consideration, and thus they are inflexible for infrastructure administrators and non-user-friendly for citizen data scientists. We propose Submarine, a unified machine learning platform, to address the challenges.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 35 references that are in the map. First posted on arXiv 22 Aug 2021.

Builds on

8 of 35 references.

References · 8

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2017DeepFM: A Factorization-Machine based Neural Network for CTR Predictioncited by 673
  4. 2017Ray: A Distributed Framework for Emerging AI Applicationscited by 516
  5. 2014One weird trick for parallelizing convolutional neural networkscited by 264
  6. 2015MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systemscited by 195
  7. 2019Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloadscited by 79
  8. 2019TonY: An Orchestrator for Distributed Machine Learning Jobscited by 1

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Citing papers per year: 2022: 1.

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