Variational Exploration Module VEM: A Cloud-Native Optimization and Validation Tool for Geospatial Modeling and AI Workflows

• URL: http://arxiv.org/abs/2311.16196v1
• Date: Sun, 26 Nov 2023 23:07:00 GMT
• Title: Variational Exploration Module VEM: A Cloud-Native Optimization and Validation Tool for Geospatial Modeling and AI Workflows
• Authors: Julian Kuehnert (1), Hiwot Tadesse (1), Chris Dearden (2), Rosie Lickorish (3), Paolo Fraccaro (3), Anne Jones (3), Blair Edwards (3), Sekou L. Remy (1), Peter Melling (4), Tim Culmer (4) ((1) IBM Research, Nairobi, Kenya, (2) STFC Hartree Centre, Warrington, UK, (3) IBM Research, Daresbury, UK, (4) Riskaware Ltd., Bristol, UK)
• Abstract summary: Cloud-based deployments help to scale up these modeling and AI. We have developed the Variational Exploration Module which facilitates the optimization and validation of modeling deployed in the cloud. The flexibility and robustness of the model-agnostic module is demonstrated using real-world applications.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Geospatial observations combined with computational models have become key to understanding the physical systems of our environment and enable the design of best practices to reduce societal harm. Cloud-based deployments help to scale up these modeling and AI workflows. Yet, for practitioners to make robust conclusions, model tuning and testing is crucial, a resource intensive process which involves the variation of model input variables. We have developed the Variational Exploration Module which facilitates the optimization and validation of modeling workflows deployed in the cloud by orchestrating workflow executions and using Bayesian and machine learning-based methods to analyze model behavior. User configurations allow the combination of diverse sampling strategies in multi-agent environments. The flexibility and robustness of the model-agnostic module is demonstrated using real-world applications.

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