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Organizations rely on machine learning engineers (MLEs) to operationalize ML, i.e., deploy and maintain ML pipelines in production.
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Curiosity, creativity, and surprise as analytic tools: Grounded theory method
Michael Muller · 2014
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Hidden technical debt in machine learning systems
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Daniel Crankshaw, Xin Wang, Guilio Zhou, Michael J Franklin, Joseph E Gonzalez, and Ion Stoica · 2017
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Ground: A data context service
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Variolite: Supporting exploratory programming by data scientists
Mary Beth Kery, Amber Horvath, and Brad A Myers · 2017
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Data scientists in software teams: State of the art and challenges
Miryung Kim, Thomas Zimmermann, Robert DeLine, and Andrew Begel · 2017
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Data management challenges in production machine learning
Neoklis Polyzotis, Sudip Roy, Steven Euijong Whang, and Martin Zinkevich · 2017
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Snorkel: Rapid training data creation with weak supervision
Alexander Ratner, Stephen H Bach, Henry Ehrenberg, Jason Fries, Sen Wu, and Christopher Ré · 2017
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What makes users trust a chatbot for customer service? an exploratory interview study
Asbjørn Følstad, Cecilie Bertinussen Nordheim, and Cato Alexander Bjørkli · 2018
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Context: The missing piece in the machine learning lifecycle
Rolando Garcia, Vikram Sreekanti, Neeraja Yadwadkar, Daniel Crankshaw, Joseph E Gonzalez, and Joseph M Hellerstein · 2018
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Trust in data science: Collaboration, translation, and accountability in corporate data science projects
Samir Passi and Steven J Jackson · 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
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Automating large-scale data quality verification
Sebastian Schelter et al · 2018
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Accelerating the machine learning lifecycle with mlflow
M. Zaharia et al · 2018
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Software engineering for machine learning: A case study
Saleema Amershi, Andrew Begel, Christian Bird, Robert DeLine, Harald Gall, Ece Kamar, Nachiappan Nagappan, Besmira Nushi, and Thomas Zimmermann · 2019
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Data debugging and exploration with vizier
Mike Brachmann, Carlos Bautista, Sonia Castelo, Su Feng, Juliana Freire, Boris Glavic, Oliver Kennedy, Heiko Müeller, Rémi Rampin, William Spoth, and Ying Yang · 2019
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Data validation for machine learning
Eric Breck, Marty Zinkevich, Neoklis Polyzotis, Steven Whang, and Sudip Roy · 2019
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Managing messes in computational notebooks
Andrew Head, Fred Hohman, Titus Barik, Steven M. Drucker, and Robert DeLine · 2019
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Improving fairness in machine learning systems: What do industry practitioners need?
Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé, Miro Dudik, and Hanna Wallach · 2019
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A survey of devops concepts and challenges
What is mlops?
Sridhar Alla and Suman Kalyan Adari · 2021
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Hindsight logging for model training
Rolando Garcia, Eric Liu, Vikram Sreekanti, Bobby Yan, Anusha Dandamudi, Joseph E. Gonzalez, Joseph M Hellerstein, and Koushik Sen · 2021
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On continuous integration / continuous delivery for automated deployment of machine learning models using mlops
Satvik Garg, Pradyumn Pundir, Geetanjali Rathee, P.K. Gupta, Somya Garg, and Saransh Ahlawat · 2021
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Mlops challenges and how to face them, Aug 2021
Samadrita Ghosh · 2021
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Mlinspect: A data distribution debugger for machine learning pipelines
Stefan Grafberger, Shubha Guha, Julia Stoyanovich, and Sebastian Schelter · 2021
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Towards mlops: A framework and maturity model
Meenu Mary John, Helena Holmström Olsson, and Jan Bosch · 2021
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Leonardo Leite, Carla Rocha, Fabio Kon, Dejan Milojicic, and Paulo Meirelles · 2019
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Devops in practice: A multiple case study of five companies
Lucy Ellen Lwakatare, Terhi Kilamo, Teemu Karvonen, Tanja Sauvola, Ville Heikkilä, Juha Itkonen, Pasi Kuvaja, Tommi Mikkonen, Markku Oivo, and Casper Lassenius · 2019
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A taxonomy of software engineering challenges for machine learning systems: An empirical investigation
Lucy Ellen Lwakatare, Aiswarya Raj, J. Bosch, Helena Holmström Olsson, and Ivica Crnkovic · 2019
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How data science workers work with data: Discovery, capture, curation, design, creation
Michael Muller, Ingrid Lange, Dakuo Wang, David Piorkowski, Jason Tsay, Q Vera Liao, Casey Dugan, and Thomas Erickson · 2019
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Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Yaniv Ovadia, Emily Fertig, J. Ren, Zachary Nado, D. Sculley, Sebastian Nowozin, Joshua V. Dillon, Balaji Lakshminarayanan, and Jasper Snoek · 2019
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Failing loudly: An empirical study of methods for detecting dataset shift
Stephan Rabanser, Stephan Günnemann, and Zachary Chase Lipton · 2019
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Human-ai collaboration in data science: Exploring data scientists’ perceptions of automated ai
Dakuo Wang, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, and Alexander Gray · 2019
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The CLEAR benchmark: Continual LEArning on real-world imagery
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Fine-grained lineage for safer notebook interactions
Stephen Macke, Hongpu Gong, Doris Jung-Lin Lee, Andrew Head, Doris Xin, and Aditya Parameswaran · 2021
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Who needs mlops: What data scientists seek to accomplish and how can mlops help?
Sasu Mäkinen, Henrik Skogström, Eero Laaksonen, and Tommi Mikkonen · 2021
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Global mlops and ml tools landscape: Mlreef, Feb 2021
MLReef · 2021
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Adoption of machine learning systems for medical diagnostics in clinics: qualitative interview study
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A data quality-driven view of mlops
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“everyone wants to do the model work, not the data work”: Data cascades in high-stakes ai
Nithya Sambasivan, Shivani Kapania, Hannah Highfill, Diana Akrong, Praveen Paritosh, and Lora M Aroyo · 2021
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A fine-grained analysis on distribution shift
Olivia Wiles, Sven Gowal, Florian Stimberg, Sylvestre-Alvise Rebuffi, Ira Ktena, Krishnamurthy Dvijotham, and Ali Taylan Cemgil · 2021
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Production machine learning pipelines: Empirical analysis and optimization opportunities
Doris Xin, Hui Miao, Aditya Parameswaran, and Neoklis Polyzotis · 2021
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Whither automl? understanding the role of automation in machine learning workflows
Doris Xin, Eva Yiwei Wu, Doris Jung-Lin Lee, Niloufar Salehi, and Aditya Parameswaran · 2021
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A machine learning model helps process interviewer comments in computer-assisted personal interview instruments: A case study
Catherine Billington, Gonzalo Rivero, Andrew Jannett, and Jiating Chen · 2022
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Machine learning operations (mlops): Overview, definition, and architecture, 2022
Dominik Kreuzberger, Niklas Kühl, and Sebastian Hirschl · 2022
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Practices and infrastructures for ml systems–an interview study in finnish organizations
Dennis Muiruri, Lucy Ellen Lwakatare, Jukka K Nurminen, and Tommi Mikkonen · 2022
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Challenges in deploying machine learning: A survey of case studies
Andrei Paleyes, Raoul-Gabriel Urma, and Neil D. Lawrence · 2022
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Rethinking streaming machine learning evaluation
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Towards observability for production machine learning pipelines
Shreya Shankar and Aditya G. Parameswaran · 2022
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Why ai investments fail to deliver, 2022
Steve Nunez · 2022
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Mlops — Wikipedia, the free encyclopedia, 2022
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Bolt-on, compact, and rapid program slicing for notebooks
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