Get an early look at a feature that would allow business users, analysts, or modelers to change output data, recompute statistics, and compare to another model run.
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Feedback on any project is inevitable. With optimization projects and decision models, we're developing more efficient commenting and feedback loops that sit alongside modeling work.
Interested in Hexaly to solve optimization problems? This techtalk covers how to get started with Hexaly in Nextmv, use features for scenario testing, best plan auto-selection, model management, and a Q&A with Hexaly’s CEO.
Why did the model choose that plan? How much better is the optimization’s plan than mine? Nextmv is helping operators, analysts, and business users more efficiently answer these questions with new UI features that improve decision model explainability.
In under ten minutes, we demo how to deploy and run your local Python decision model from a Jupyter Notebook as a fully featured decision app on the Nextmv platform for simpler collaboration and a smarter workflow.
Go from a local decision model to a remotely hosted decision application (with API endpoints) that you can easily share, test, and confidently promote to production. See the model-to-app workflow using a Jupyter Notebook and Nextmv in under 30 minutes.
