Paper¶
2015-Google-Hidden technical debt in machine learning systems¶
机器学习系统中的技术负债1
TODO: 2021-Microsoft-A Data Quality-Driven View of MLOps¶
Data-Centric AI推荐的论文，介绍了数据质量在 MLOps 中的重要性2.
TODO: 2022-IBM-Advances in Exploratory Data Analysis, Visualisation and Quality for Data Centric AI Systems¶
Data-Centric AI推荐的论文，也是 Data Centric AI Systems 相关的介绍3.
2023-Scaling ML Products At Startups: A Practitioner’s Guide¶
讨论创业公司从开发到规模化部署机器学习产品时可能遇到的问题与相关的成本评估4.
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D. Sculley et al., “Hidden technical debt in machine learning systems,” Advances in neural information processing systems, vol. 28, 2015. ↩
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C. Renggli, L. Rimanic, N. M. Gürel, B. Karlaš, W. Wu, and C. Zhang, “A data quality-driven view of mlops,” arXiv preprint arXiv:2102.07750, 2021. ↩
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H. Patel, S. Guttula, R. S. Mittal, N. Manwani, L. Berti-Equille, and A. Manatkar, “Advances in exploratory data analysis, visualisation and quality for data centric AI systems,” in Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining, 2022, pp. 4814–4815. ↩
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A. Dhingra and G. Sood, “Scaling ML products at startups: A practitioner’s guide,” arXiv preprint arXiv:2304.10660, 2023. ↩