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Found 5 papers, 0 papers with code
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.
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.
DeepDownscale: a Deep Learning Strategy for High-Resolution Weather Forecast
Running high-resolution physical models is computationally expensive and essential for many disciplines.
A Scalable Machine Learning System for Pre-Season Agriculture Yield Forecast
Yield forecast is essential to agriculture stakeholders and can be obtained with the use of machine learning models and data coming from multiple sources.
