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?

Optimization is often highlighted in supply chain contexts – routing, scheduling, inventory, and other tangible, visible problems. This post explores a few use cases in computing and software.

Decision model development and collaboration is easier and faster when there’s a shared system of record to reference and interact with for I/O, results, and charts. Learn how to create one in Nextmv.

We’ve partnered with FICO® Xpress Solver to provide a modern DecisionOps platform experience to FICO Xpress users, allowing them to create end-to-end decision pipelines for model deployment, testing, collaboration, and monitoring at scale.

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.

This interview explores perspectives on growth and adoption of optimization across practitioners, challenges and opportunities in the optimization space, thinking about optimization in broader AI strategies, and DecisionOps + Hexaly.

Create and manage queues directly in Nextmv to easily prioritize model runs. Configure prioritization at runtime to prevent blocking the queue with large runs or at the model version level to ensure production runs have precedence.

When Veho’s data science team wanted to evolve their route optimization with a new solver, they leveraged Nextmv’s DecisionOps platform to transition to Hexaly in a matter of weeks.

Tune your Gurobipy and scikit-learn models with configurable options so your team can prototype faster, experiment with ease, and manage their models with more insight on Nextmv’s DecisionOps platform.

Looking for an MLflow experience for optimization? Create and manage end-to-end decision workflows in Nextmv with steps such as data prep, regression, optimization, and visualization. Press play to kick off an automated workflow and see live progress via a flow diagram and logs in the Nextmv UI.

What is the relationship between price, profit, and waste? Let’s find out using a Statsmodels ML regressor, a Gurobipy optimizer, Plotly charts, scenario testing, and a slick decision workflow built and run in Nextmv.

Identify better plans by replaying your run with varying settings. Create multiple scenarios to understand the impact of input data, model configuration, and more on your KPIs in Nextmv’s UI.