Observability & decision science: Monitoring optimization model performance and more
When decision models power real-life operations, any sort of model performance failure is a nightmare. Learn why observability in the operations research space is often a challenge – and how to give your team more visibility into model performance with DecisionOps.CI/CD for decision science: What is it, how does it work, and why does it matter?
CI/CD is how you ship higher quality code to production faster. But with decision science models, there’s a twist. We’ll look at the role of decision quality, specific testing tooling, and problem drift on CI/CD for decision science models.What is shadow testing for optimization models and decision algorithms?
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.What is scenario testing for optimization models?
Welcome to the what-if wonderland of parallel universes. What if you expand your delivery region? Or fulfill orders from stores and distribution centers? Or hire more staff? Scenario tests help provide the answers. And, no, it’s not the same as simulation.What is acceptance testing for optimization models?
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.
