{"total":20,"rows":[{"id":"2110.10234","title":"Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process","created":"2021-10-19","cited_by_count":56,"area":null},{"id":"2102.07750","title":"A Data Quality-Driven View of MLOps","created":"2021-02-15","cited_by_count":31,"area":null},{"id":"2112.03865","title":"Universalizing Weak Supervision","created":"2021-12-07","cited_by_count":27,"area":null},{"id":"2210.11831","title":"Management of Machine Learning Lifecycle Artifacts: A Survey","created":"2022-10-21","cited_by_count":13,"area":null},{"id":"2203.01382","title":"Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming","created":"2022-03-02","cited_by_count":10,"area":null},{"id":"2211.13375","title":"Lifting Weak Supervision To Structured Prediction","created":"2022-11-24","cited_by_count":10,"area":null},{"id":"2110.07554","title":"Looper: An end-to-end ML platform for product decisions","created":"2021-10-14","cited_by_count":9,"area":null},{"id":"2602.09572","title":"Predictive Query Language: A Domain-Specific Language for Predictive Modeling on Relational Databases","created":"2026-02-10","cited_by_count":8,"area":null},{"id":"2106.01501","title":"Ember: No-Code Context Enrichment via Similarity-Based Keyless Joins","created":"2021-06-02","cited_by_count":7,"area":null},{"id":"2208.14362","title":"AutoWS-Bench-101: Benchmarking Automated Weak Supervision with 100 Labels","created":"2022-08-30","cited_by_count":7,"area":null},{"id":"2108.05053","title":"Managing ML Pipelines: Feature Stores and the Coming Wave of Embedding Ecosystems","created":"2021-08-11","cited_by_count":6,"area":null},{"id":"2408.09196","title":"Maintainability Challenges in ML: A Systematic Literature Review","created":"2024-08-17","cited_by_count":5,"area":null},{"id":"2107.08148","title":"Declarative Machine Learning Systems","created":"2021-07-16","cited_by_count":4,"area":null},{"id":"2410.21346","title":"Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach","created":"2024-10-28","cited_by_count":4,"area":null},{"id":"2306.16913","title":"AutoML in Heavily Constrained Applications","created":"2023-06-29","cited_by_count":3,"area":null},{"id":"2605.17172","title":"OpenJarvis: Personal AI, On Personal Devices","created":"2026-05-16","cited_by_count":3,"area":null},{"id":"2201.05026","title":"Fantastic Data and How to Query Them","created":"2022-01-13","cited_by_count":1,"area":null},{"id":"2402.01867","title":"Leveraging Large Language Models for Structure Learning in Prompted Weak Supervision","created":"2024-02-02","cited_by_count":1,"area":null},{"id":"2203.12023","title":"Generative Modeling Helps Weak Supervision (and Vice Versa)","created":"2022-03-22","cited_by_count":0,"area":null},{"id":"2504.11079","title":"Scalability and Maintainability Challenges and Solutions in Machine Learning: Systematic Literature Review","created":"2025-04-15","cited_by_count":0,"area":null}],"next_cursor":null}