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.
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A look at how Nextmv’s ML/OR connectors better optimize the plans generated by decision models through streamlining the incorporation of machine learning outputs such as forecasts. Plus, avocados are involved.
How do optimization teams get decision models live into business processes faster as managed services? We explore this through the lens of dedicated DecisionOps workflows.
If you develop decision models in Python, this presentation will save you time (and the added effort of building and maintaining DecisionOps tools). Accelerate development of your optimization models with features for testing, deploying, managing, and collaborating.
What approaches are available to decision scientists and operations researchers to incorporate more randomness and uncertainty into their models? We explore this, ML + OR, and stochastic optimization with Nextmv and Seeker.
Simulate scenarios to answer "what if" questions with your decision model.