{"total":37,"rows":[{"id":"2110.01167","title":"Trustworthy AI: From Principles to Practices","created":"2021-10-04","cited_by_count":207,"area":null},{"id":"2205.02302","title":"Machine Learning Operations (MLOps): Overview, Definition, and Architecture","created":"2022-05-04","cited_by_count":128,"area":null},{"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":"2205.05057","title":"Sensible AI: Re-imagining Interpretability and Explainability using Sensemaking Theory","created":"2022-05-10","cited_by_count":32,"area":null},{"id":"2201.00162","title":"MLOps -- Definitions, Tools and Challenges","created":"2022-01-01","cited_by_count":25,"area":null},{"id":"2209.09125","title":"Operationalizing Machine Learning: An Interview Study","created":"2022-09-16","cited_by_count":25,"area":null},{"id":"2304.00078","title":"A Meta-Summary of Challenges in Building Products with ML Components -- Collecting Experiences from 4758+ Practitioners","created":"2023-03-31","cited_by_count":22,"area":null},{"id":"2308.10908","title":"MLOps: A Review","created":"2023-08-19","cited_by_count":16,"area":null},{"id":"2211.05764","title":"DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systems","created":"2022-11-09","cited_by_count":14,"area":null},{"id":"2301.09001","title":"The Pipeline for the Continuous Development of Artificial Intelligence Models -- Current State of Research and Practice","created":"2023-01-21","cited_by_count":14,"area":null},{"id":"2306.01904","title":"Overcoming the Stability Gap in Continual Learning","created":"2023-06-02","cited_by_count":13,"area":null},{"id":"2206.02868","title":"A Human-Centric Take on Model Monitoring","created":"2022-06-06","cited_by_count":9,"area":null},{"id":"2304.03674","title":"Machine Learning with Requirements: a Manifesto","created":"2023-04-07","cited_by_count":8,"area":null},{"id":"2206.00335","title":"Putting AI Ethics into Practice: The Hourglass Model of Organizational AI Governance","created":"2022-06-01","cited_by_count":7,"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":"2211.06409","title":"Capabilities for Better ML Engineering","created":"2022-11-11","cited_by_count":3,"area":null},{"id":"2506.02032","title":"Towards Secure MLOps: Surveying Attacks, Mitigation Strategies, and Research Challenges","created":"2025-05-30","cited_by_count":3,"area":null},{"id":"2106.13265","title":"Disease Progression Modeling Workbench 360","created":"2021-06-24","cited_by_count":2,"area":null},{"id":"2209.11453","title":"A Preliminary Investigation of MLOps Practices in GitHub","created":"2022-09-23","cited_by_count":2,"area":null},{"id":"2302.14139","title":"Scalable End-to-End ML Platforms: from AutoML to Self-serve","created":"2023-02-27","cited_by_count":2,"area":null},{"id":"2305.02474","title":"MLHOps: Machine Learning for Healthcare Operations","created":"2023-05-04","cited_by_count":2,"area":null},{"id":"2305.19298","title":"MLOps: A Step Forward to Enterprise Machine Learning","created":"2023-05-27","cited_by_count":2,"area":null},{"id":"2312.02337","title":"Measuring Distributional Shifts in Text: The Advantage of Language Model-Based Embeddings","created":"2023-12-04","cited_by_count":2,"area":null},{"id":"2402.12867","title":"Towards MLOps: A DevOps Tools Recommender System for Machine Learning System","created":"2024-02-20","cited_by_count":2,"area":null},{"id":"2205.13060","title":"Designing an Efficient End-to-end Machine Learning Pipeline for Real-time Empty-shelf Detection","created":"2022-05-25","cited_by_count":1,"area":null},{"id":"2305.08093","title":"AI for Agile development: a Meta-Analysis","created":"2023-05-14","cited_by_count":1,"area":null},{"id":"2409.03805","title":"Exploratory Visual Analysis for Increasing Data Readiness in Artificial Intelligence Projects","created":"2024-09-05","cited_by_count":1,"area":null},{"id":"2409.09742","title":"OML-AD: Online Machine Learning for Anomaly Detection in Time Series Data","created":"2024-09-15","cited_by_count":1,"area":null},{"id":"2409.19416","title":"Machine Learning Operations: A Mapping Study","created":"2024-09-28","cited_by_count":1,"area":null},{"id":"2110.03923","title":"Opportunities for Machine Learning to Accelerate Halide Perovskite Commercialization and Scale-Up","created":"2021-10-08","cited_by_count":0,"area":null},{"id":"2302.01061","title":"MLOps with enhanced performance control and observability","created":"2023-02-02","cited_by_count":0,"area":null},{"id":"2404.14326","title":"Machine Learning Techniques for MRI Data Processing at Expanding Scale","created":"2024-04-22","cited_by_count":0,"area":null},{"id":"2405.06835","title":"Automating Code Adaptation for MLOps -- A Benchmarking Study on LLMs","created":"2024-05-10","cited_by_count":0,"area":null},{"id":"2501.08402","title":"Addressing Quality Challenges in Deep Learning: The Role of MLOps and Domain Knowledge","created":"2025-01-14","cited_by_count":0,"area":null},{"id":"2502.17440","title":"GenAIOps for GenAI Model-Agility","created":"2024-12-19","cited_by_count":0,"area":null},{"id":"2509.24224","title":"Proposing a Framework for Machine Learning Adoption on Legacy Systems","created":"2025-09-29","cited_by_count":0,"area":null},{"id":"2610.01282","title":"Trustworthy Data- and ML-Ops for Intelligent Transportation Systems and Logistics","created":"2026-10-01","cited_by_count":0,"area":null}],"next_cursor":null}