English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Machine Learning (ML) has become essential in several industries. InComputational Science and Engineering (CSE), the complexity of the ML lifecyclecomes from the large variety of data, scientists' expertise, tools, andworkflows. If data are not tracked properly during the lifecycle, it becomesunfeasible to recreate a ML model from scratch or to explain to stakeholdershow it was created. The main limitation of provenance tracking solutions isthat they cannot cope with provenance capture and integration of domain and MLdata processed in the multiple workflows in the lifecycle while keeping theprovenance capture overhead low. To handle this problem, in this paper wecontribute with a detailed characterization of provenance data in the MLlifecycle in CSE; a new provenance data representation, called PROV-ML, builton top of W3C PROV and ML Schema; and extensions to a system that tracksprovenance from multiple workflows to address the characteristics of ML andCSE, and to allow for provenance queries with a standard vocabulary. We show apractical use in a real case in the Oil and Gas industry, along with itsevaluation using 48 GPUs in parallel.

Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering