How confident are you that your model's parameters are actually right? Most teams tune by gut: change a number, run the model, eyeball the output, repeat. It works until it doesn't, and when it doesn't, you get plans that are mathematically optimal but operationally broken. The tools exist to do this systematically: sweep parameters in a single test run, set guardrails that catch regressions before they ship, and deploy multiple configurations that auto-select the best result per request.
In this 30-minute session, we’ll walk through how to use Nextmv to tune objective function parameters (e.g., penalties, clustering, and balance weights), replay realistic “what if” questions with scenario tests, and use ensemble definitions to auto-select the best plan based on your KPIs. We’ll also show how the Nextmv MCP server can accelerate testing and tuning by powering high-throughput evaluation workflows for your models. Using a real-world example, we’ll show how to move from “it seems better” to measurable improvements in KPIs. We’ll wrap with Q&A, so bring your questions!
Key segments
- Parameter tuning workflows vs guess-and-check
- Scenario testing to explore “what if” questions
- Using ensemble definitions to auto-select the “best” plan
- Leveraging the Nextmv MCP server to accelerate testing and tuning
Get started on Nextmv for free and learn more in the documentation.
Have questions? Reach out to us to talk with our technical team.
