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SysML: The New Frontier of Machine Learning Systems

  • Training System

    • Low, Y., Gonzalez, J., Kyrola, A., Bickson, D., Guestrin, C., & Hellerstein, J. M. (2012). Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud. PVLDB, 5(8), 716–727.
    • Li, M., Andersen, D. G., Park, J. W., Ahmed, A., Josifovski, V., Long, J., … Ahmed, A. (2014). Scaling Distributed Machine Learning with the Parameter Server. In OSDI (pp. 583–598).
    • Boehm, M., Surve, A. C., Tatikonda, S., Dusenberry, M. W., Eriksson, D., Evfimievski, A. V., … Sen, P. (2016). SystemML: Declarative Machine Learning on Spark. PVLDB, 9(13), 1425–1436.
    • Parameter Server
      • Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., … Zheng, X. (2016). TensorFlow: A System for Large-Scale Machine Learning. In OSDI (pp. 265–284).
      • Huang, Y., Jin, T., Wu, Y., Cai, Z., Yan, X., Yang, F., … Cheng, J. (2018). FlexPS: Flexible parallelism control in parameter server architecture. Proceedings of the VLDB Endowment, 11(5), 566–579.

  • Inference/Serving System

    • Lee, Y., Scolari, A., Chun, B.-G., Santambrogio, M. D., Weimer, M., & Interlandi, M. (2018). PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems. In OSDI.
    • Crankshaw, D., Wang, X., Zhou, G., Franklin, M. J., Gonzalez, J. E., Zhou, G., & Stoica, I. (2017). Clipper: A Low-Latency Online Prediction Serving System. In NSDI.
    • Olston, C., & Harmsen, J. (2017). TensorFlow-Serving: Flexible, High-Performance ML Serving. In NIPS (pp. 1–8).
    • Crankshaw, D., Bailis, P., Gonzalez, J. E., Li, H., Zhang, Z., Franklin, M. J., … Jordan, M. I. (2015). The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox. In CIDR.

  • ML Pipelines

    • Shang, Z., Zgraggen, E., Buratti, B., Chung, Y., Eichmann, P., Binnig, C., … Kraska, T. (2019). Democratizing Data Science through Interactive Curation of ML Pipelines. In SIGMOD Conference (pp. 1171–1188).
    • Kunft, A., Katsifodimos, A., & Schelter, S. (2019). An Intermediate Representation for Optimizing Machine Learning Pipelines. PVLDB, 12(11), 1553–1567.
    • Xin, D., Macke, S., Ma, L., Liu, J., Song, S., & Parameswaran, A. (2018). Helix: Holistic Optimization for Accelerating Iterative Machine Learning. PVLDB, 12(4), 446–460.

  • Reinforcement Learning

    • Moritz, P., Nishihara, R., Wang, S., Tumanov, A., Liaw, R., Liang, E., … Stoica, I. (2018). Ray : A Distributed Framework for Emerging AI Applications. In OSDI (pp. 561–577).

  • Relation with General-purpose Frameworks

    • Zhang, Z., Cui, B., Shao, Y., Yu, L., Jiang, J., & Miao, X. (2019). PS2: Parameter Server on Spark. In Proceedings of the 2019 International Conference on Management of Data - SIGMOD ’19 (pp. 376–388).
    • Aurick Qiao, Abutalib Aghayev, Weiren Yu, Haoyang Chen, Qirong Ho, Garth A. Gibson, Eric P. Xing: Litz: Elastic Framework for High-Performance Distributed Machine Learning. USENIX Annual Technical Conference 2018: 631-644
    • Xing, E. P., Yu, Y., Ho, Q., Dai, W., Kim, J.-K., Wei, J., … Kumar, A. (2015). Petuum: A New Platform for Distributed Machine Learning on Big Data. KDD ’15 (pp. 1335–1344).