Select multiple runs from your model’s run history for a side-by-side comparison of their KPIs and model configuration. Answer performance questions in a few clicks and determine what to test next to continue improving your model.
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You’ve got a production decision model and an updated decision model. Should you ship the new model to prod? Will the new model meet your acceptable KPI thresholds? Acceptance testing provides the answers by delivering a documented, repeatable decision-making process.
Do you launch decision model A or B? What happens if an operator makes a manual override? How does a new optimization model perform against real-world data? Testing and experimentation has the answers, but getting them has traditionally been a challenge.
Create a release process for your custom model that mirrors your experience for shipping any other software. Develop locally, deploy to operational environments, test performance, use version control for easy rollbacks, and iterate with confidence.
We built a docs site that references code samples from external files to avoid copy-paste errors, makes it easier to manage code updates over time, and allows for separate testing processes to be run to validate referenced code. Here’s how.
This post explains what order fulfillment is, considerations for model constraints and objective functions, and walks you through building a custom optimization model, step by step.
