Machine learning (ML) is being applied in a number of everyday contexts from image recognition, to natural language processing, to autonomous vehicles, to product recommendation. In the science realm, ML is being used for medical diagnosis, new materials development, smart agriculture, DNA classification, and many others. In this article, we describe the opportunities of using ML in the area of scientific workflow management. Scientific workflows are key to today’s computational science, enabling the definition and execution of complex applications in heterogeneous and often distributed environments. We describe the challenges of composing and executing scientific workflows and identify opportunities for applying ML techniques to meet these challenges by enhancing the current workflow management system capabilities. We foresee that as the ML field progresses, the automation provided by workflow management systems will greatly increase and result in significant improvements in scientific productivity.
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mlr3pipelines - Flexible Machine Learning Pipelines in R
Recent years have seen a proliferation of ML frameworks. Such systems make ML accessible to non-experts, especially when combined with powerful parameter tuning and AutoML techniques. Modern, applied ML extends beyond direct learning on clean data, however, and needs an expressive language for the construction of complex ML workflows beyond simple pre- and post-processing. We present mlr3pipelines, an R framework which can be used to define linear and complex non-linear ML workflows as directed acyclic graphs. The framework is part of the mlr3 ecosystem, leveraging convenient resampling, benchmarking, and tuning components.
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- Award ID(s):
- 1813537
- NSF-PAR ID:
- 10289803
- Date Published:
- Journal Name:
- Journal of machine learning research
- Volume:
- 22
- Issue:
- 184
- ISSN:
- 1532-4435
- Page Range / eLocation ID:
- 1-7
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Computational workflows are widely used in data analysis, enabling automated tracking of steps and storage of provenance information, leading to innovation and decision-making in the scientific community. However, the growing popularity of workflows has raised concerns about reproducibility and reusability which can hinder collaboration between institutions and users. In order to address these concerns, it is important to standardize workflows or provide tools that offer a framework for describing workflows and enabling computational reusability. One such set of standards that has recently emerged is the Common Workflow Language (CWL), which offers a robust and flexible framework for data analysis tools and workflows. To promote portability, reproducibility, and interoperability of AI/ML workflows, we developed
geoweaver_cwl geoweaver_cwl geoweaver_cwl https://pypi.org/project/geoweaver-cwl/0.0.1/ and exemplar results are accessible at:https://github.com/amrutakale08/geoweaver_cwl-usecases .
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- The DOI is not currently available.
