  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.
