Testing optimization models improves workflows, increases stakeholder buy-in, and helps teams deploy to production safely and quickly. But what are the steps to make testing repeatable and scalable?
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Shadow testing isn’t just for the MLOps crowd. Decision algorithm teams can have it too for their optimization models solving routing, scheduling, allocation, and fulfillment problems across supply chain, retail, healthcare, and more.
Every time you run your app, Nextmv captures metadata that provides insight into how the app is performing. Use a simple script to pull that run data into Google Sheets to analyze and visualize KPIs over time.
You’re pretty sure your staging model is ready for production. Pretty sure... But you want to test it under real-world conditions without real-world impact. Shadow testing gives your “understudy” algorithm the stage to prove its mettle.
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We are excited to release the public beta of our new out-of-the-box routing engine that lets users solve VRPs with a low-code SaaS – and use the same model to customize and incorporate testing into their dev workflows.
Decision optimization technology plays a critical role in everyday operations — food delivery, healthcare staffing, and subscription box assembly — but it can often stand in its own way. It doesn’t have to.
