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Machine learning (ML) is now commonplace, powering data-driven applications in various organizations.
The earth mover’s distance as a metric for image retrieval
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Xian-Ling Mao, Bo-Si Feng, Yi-Jing Hao, Liqiang Nie, Heyan Huang, and Guihua Wen. 2017 · 2017
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noWorkflow: a Tool for Collecting, Analyzing, and Managing Provenance from Python Scripts
João Felipe Pimentel, Leonardo Murta, Vanessa Braganholo, and Juliana Freire. 2017 · 2017
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Neoklis Polyzotis, Sudip Roy, Steven Euijong Whang, and Martin Zinkevich. 2017 · 2017
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Sebastian Schelter, Joos-Hendrik Boese, Johannes Kirschnick, Thoralf Klein, and Stephan Seufert. 2017 · 2017
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ProvDB: Provenance-enabled Lifecycle Management of Collaborative Data Analysis Workflows
Hui Miao and Amol Deshpande. 2018 · 2018
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Data lifecycle challenges in production machine learning: a survey
Neoklis Polyzotis, Sudip Roy, Steven Euijong Whang, and Martin Zinkevich. 2018 · 2018
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On Challenges in Machine Learning Model Management
Sebastian Schelter, Felix Biessmann, Tim Januschowski, David Salinas, Stephan Seufert, Gyuri Szarvas, Manasi Vartak, Samuel Madden, Hui Miao, Amol Deshpande, et al · 2018
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Runway - Model Lifecycle Management at Netflix. USENIX Association
Eugen Cepoi and Liping Peng. 2020 · 2020
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Demystifying a Dark Art: Understanding Real-World Machine Learning Model Development
Angela Lee, Doris Xin, Doris Lee, and Aditya Parameswaran. 2020 · 2020
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Edo Liberty, Zohar Karnin, Bing Xiang, Laurence Rouesnel, Baris Coskun, Ramesh Nallapati, Julio Delgado, Amir Sadoughi, Yury Astashonok, Piali Das, Can Balioglu, Saswata Chakravarty, Madhav Jha, Philip Gautier, David Arpin, Tim Januschowski, Valentin Flunkert, Yuyang Wang, Jan Gasthaus, Lorenzo Stella, Syama Rangapuram, David Salinas, Sebastian Schelter, and Alex Smola. 2020 · 2020
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The Relational Data Borg is Learning
Dan Olteanu. 2020 · 2020
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Improving Reproducibility of Data Science Pipelines through Transparent Provenance Capture
Lukas Rupprecht, James C. Davis, Constantine Arnold, Yaniv Gur, and Deepavali Bhagwat. 2020 · 2020
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How do data science workers collaborate? roles, workflows, and tools
Amy X Zhang, Michael Muller, and Dakuo Wang. 2020 · 2020
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