Production Machine Learning Pipelines: Empirical Analysis and Optimization Opportunities. Xin, D., Miao, H., Parameswaran, A. G., & Polyzotis, N. In Li, G., Li, Z., Idreos, S., & Srivastava, D., editors, SIGMOD '21: International Conference on Management of Data, Virtual Event, China, June 20-25, 2021, pages 2639–2652, 2021. ACM.
Production Machine Learning Pipelines: Empirical Analysis and Optimization Opportunities [link]Paper  doi  bibtex   
@inproceedings{DBLP:conf/sigmod/XinMPP21,
  author       = {Doris Xin and
                  Hui Miao and
                  Aditya G. Parameswaran and
                  Neoklis Polyzotis},
  editor       = {Guoliang Li and
                  Zhanhuai Li and
                  Stratos Idreos and
                  Divesh Srivastava},
  title        = {Production Machine Learning Pipelines: Empirical Analysis and Optimization
                  Opportunities},
  booktitle    = {{SIGMOD} '21: International Conference on Management of Data, Virtual
                  Event, China, June 20-25, 2021},
  pages        = {2639--2652},
  publisher    = {{ACM}},
  year         = {2021},
  url          = {https://doi.org/10.1145/3448016.3457566},
  doi          = {10.1145/3448016.3457566},
  timestamp    = {Sun, 02 Oct 2022 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/conf/sigmod/XinMPP21.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

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