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Automated Management of Deep Learning Experiments
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Automated Management of Deep Learning Experiments (2019)Automated Management of Deep Learning Exp…Provenance Analysis for Missing Answers and Integrity Repairs. (2018)Provenance Analysis for Missi…ModelHub: Deep Learning Lifecycle Management (2017)ModelHub: Deep Learning Lifec…Data Management Challenges in Production Machine Learning (2017)Data Management Challenges in…Automatically Tracking Metadata and Provenance of Machine Learning Experiments (2017)Automatically Tracking Metada…M odel DB (2016)M odel DBnoWorkflow: Capturing and Analyzing Provenance of Scripts (2015)noWorkflow: Capturing and Ana…DLProv: A Data-Centric Support for Deep Learning Workflow Analyses (2024)DLProv: A Data-Centric Suppor…Scientific workflow management and the Kepler system (2005)VisTrails (2006)The Taverna workflow suite: designing and executing workflows of Web Services on the desktop, web or in the cloud (2013)Probabilistic demand forecasting at scale (2017)Automating large-scale data quality verification (2018)Artificial Intelligence Governance For Businesses (2022)DataHub: Collaborative Data Science & Dataset Version Management at Scale (2014)MODELDB: A System for Machine Learning Model Management. (2017)MODELDB: Opportunities and Challenges in Managing Machine Learning Models. (2018)Vamsa: Automated Provenance Tracking in Data Science Scripts (2020)Workflow provenance in the lifecycle of scientific machine learning (2021)Provenance for Computational Tasks: A Survey (2008)YesWorkflow: A User-Oriented, Language-Independent Tool for Recovering Workflow Information from Scripts (2015)ReproZip (2016)Sustainable computational science: the ReScience initiative (2017)A cost-based model and effective heuristic for repairing constraints by value modification (2005)Conditional functional dependencies for capturing data inconsistencies (2008)On approximating optimum repairs for functional dependency violations (2009)LIMA: Fine-grained Lineage Tracing and Reuse in Machine Learning Systems (2021)MLCask: Efficient Management of Component Evolution in Collaborative Data Analytics Pipelines (2021)OmniFair: A Declarative System for Model-Agnostic Group Fairness in Machine Learning (2021)Provenance and scientific workflows (2008)Developments in MLflow (2020)Workflow Provenance in the Computing Continuum for Responsible, Trustworthy, and Energy-Efficient AI (2024)DLProv: a suite of provenance services for deep learning workflow analyses (2025)Towards Model-based Pricing for Machine Learning in a Data Marketplace (2019)Data collection and quality challenges for deep learning (2020)CleanML: A Study for Evaluating the Impact of Data Cleaning on ML Classification Tasks (2021)Improving reproducibility of data science pipelines through transparent provenance capture (2020)Production Machine Learning Pipelines (2021)
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References

  • 2017Data Management Challenges in Production Machine Learning214 cited
  • 2016M odel DB157 cited
  • 2015noWorkflow: Capturing and Analyzing Provenance of Scripts129 cited
  • 2018Provenance Analysis for Missing Answers and Integrity Repairs.112 cited
  • 2017Automatically Tracking Metadata and Provenance of Machine Learning Experiments78 cited
  • 2017ModelHub: Deep Learning Lifecycle Management61 cited

Cited by

  • 2024DLProv: A Data-Centric Support for Deep Learning Workflow Analyses17 cited

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