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Found 6 papers, 1 papers with code
Towards Lightweight Data Integration using Multi-workflow Provenance and Data Observability
Modern large-scale scientific discovery requires multidisciplinary collaboration across diverse computing facilities, including High Performance Computing (HPC) machines and the Edge-to-Cloud continuum.
Context-aware Execution Migration Tool for Data Science Jupyter Notebooks on Hybrid Clouds
Interactive computing notebooks, such as Jupyter notebooks, have become a popular tool for developing and improving data-driven models.
Workflow Provenance in the Lifecycle of Scientific Machine Learning
We contribute with (i) characterization of the lifecycle and taxonomy for data analyses; (ii) design principles to build this view, with a W3C PROV compliant data representation and a reference system architecture; and (iii) lessons learned after an evaluation in an Oil & Gas case using an HPC cluster with 393 nodes and 946 GPUs.
Managing Data Lineage of O&G Machine Learning Models: The Sweet Spot for Shale Use Case
Machine Learning (ML) has increased its role, becoming essential in several industries.
Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering
To handle this problem, in this paper we contribute with a detailed characterization of provenance data in the ML lifecycle in CSE; a new provenance data representation, called PROV-ML, built on top of W3C PROV and ML Schema; and extensions to a system that tracks provenance from multiple workflows to address the characteristics of ML and CSE, and to allow for provenance queries with a standard vocabulary.
A Hybrid Architecture for Multi-Party Conversational Systems
Multi-party Conversational Systems are systems with natural language interaction between one or more people or systems.
