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Deep Learning Provenance Data Integration: a Practical Approach
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Deep Learning Provenance Data Integration: a Practical Approach (2023)Deep Learning Provenance Data Integration…Management of Machine Learning Lifecycle Artifacts (2023)Management of Machine Learnin…Workflow provenance in the lifecycle of scientific machine learning (2021)Workflow provenance in the li…Automated end-to-end management of the modeling lifecycle in deep learning (2021)Automated end-to-end manageme…Vamsa: Automated Provenance Tracking in Data Science Scripts (2020)Vamsa: Automated Provenance T…Capturing and querying fine-grained provenance of preprocessing pipelines in data science (2020)Capturing and querying fine-g…Data Management in Machine Learning Systems (2019)Data Management in Machine Le…Komadu: A Capture and Visualization System for Scientific Data Provenance (2015)Komadu: A Capture and Visuali…A Framework for Collecting Provenance in Data-Centric Scientific Workflows (2006)The Open Provenance Model core specification (v1.1) (2010)Provenance as Essential Infrastructure for Data Lakes (2016)Provenance Analytics for Workflow-Based Computational Experiments (2018)Toward data lakes as central building blocks for data management and analysis (2022)Provenance in Scientific Workflow Systems (2007)YesWorkflow: A User-Oriented, Language-Independent Tool for Recovering Workflow Information from Scripts (2015)Exploration and Explanation in Computational Notebooks (2018)Capturing end-to-end provenance for machine learning pipelines (2024)DLProv: A Data-Centric Support for Deep Learning Workflow Analyses (2024)Provenance and scientific workflows (2008)A survey on provenance: What for? What form? What from? (2017)FactSheets: Increasing trust in AI services through supplier's declarations of conformity (2019)Towards Lightweight Data Integration Using Multi-Workflow Provenance and Data Observability (2023)PROV-AGENT: Unified Provenance for Tracking AI Agent Interactions in Agentic Workflows (2025)M odel DB (2016)Automatically Tracking Metadata and Provenance of Machine Learning Experiments (2017)Provenance Analysis for Missing Answers and Integrity Repairs. (2018)Metadata Representations for Queryable Repositories of Machine Learning Models (2023)DLProv: a suite of provenance services for deep learning workflow analyses (2025)Big data preprocessing: methods and prospects (2016)Fairness is not static (2020)Data pipeline quality: Influencing factors, root causes of data-related issues, and processing problem areas for developers (2023)Supporting Better Insights of Data Science Pipelines with Fine-grained Provenance (2024)Incremental knowledge base construction using DeepDive (2015)Data Cleaning (2016)Data Management Challenges in Production Machine Learning (2017)OmniFair: A Declarative System for Model-Agnostic Group Fairness in Machine Learning (2021)Exploring the Intersection of Machine Learning and Big Data: A Survey (2025)
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References

  • 2023Management of Machine Learning Lifecycle Artifacts64 cited
  • 2020Vamsa: Automated Provenance Tracking in Data Science Scripts59 cited
  • 2021Workflow provenance in the lifecycle of scientific machine learning46 cited
  • 2015Komadu: A Capture and Visualization System for Scientific Data Provenance36 cited
  • 2020Capturing and querying fine-grained provenance of preprocessing pipelines in data science35 cited
  • 2021Automated end-to-end management of the modeling lifecycle in deep learning26 cited
  • 2019Data Management in Machine Learning Systems19 cited

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