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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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.
