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- W3199298957 abstract "Developing real-world Machine Learning-based Systems goes beyond algorithm development. ML algorithms are usually embedded in complex pre-processing steps and consider different stages like development, testing or deployment. Managing workflows poses several challenges, such as workflow versioning, sharing pipeline elements or optimizing individual workflow elements - tasks which are usually conducted manually by data scientists. A dataset containing 16 035 real-world Machine Learning and Data Science Workflows extracted from the ONE DATA platform (https://onelogic.de/en/one-data/) is explored and made available. Based on our analysis, we develop a representation learning algorithm using a graph-level Graph Convolutional Network with explicit residuals which exploits workflow versioning history. Moreover, this method can easily be adapted to supervised tasks and outperforms state-of-the-art approaches in NAS-bench-101 performance prediction. Another interesting application is the suggestion of component types, for which a classification baseline is presented. A slightly adapted GCN using both graph- and node-level information further improves upon this baseline. The used codebase as well as all experimental setups with results are available at https://github.com/wendli01/workflow_analysis." @default.
- W3199298957 created "2021-09-27" @default.
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- W3199298957 date "2021-01-01" @default.
- W3199298957 modified "2023-09-23" @default.
- W3199298957 title "Methods for Automatic Machine-Learning Workflow Analysis" @default.
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- W3199298957 doi "https://doi.org/10.1007/978-3-030-86517-7_4" @default.
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