Deploy, run, and test your HiGHS model from a notebook without translating to different formats like .mps or .lp files.

Deploy, run, and test a VRP model from a notebook without translating to different formats like .mps or .lp files.

Deploy, run, and test your Gurobi model from a notebook without translating to different formats like .mps or .lp files.

Join us for a tour of the newest features and get a sneak peek of what’s to come. See how our decision science platform helps teams launch and scale optimization projects faster and with more confidence.

A look at how Nextmv’s ML/OR connectors better optimize the plans generated by decision models through streamlining the incorporation of machine learning outputs such as forecasts. Plus, avocados are involved.

How do optimization teams get decision models live into business processes faster as managed services? We explore this through the lens of dedicated DecisionOps workflows.

If you develop decision models in Python, this presentation will save you time (and the added effort of building and maintaining DecisionOps tools). Accelerate development of your optimization models with features for testing, deploying, managing, and collaborating.

What approaches are available to decision scientists and operations researchers to incorporate more randomness and uncertainty into their models? We explore this, ML + OR, and stochastic optimization with Nextmv and Seeker.

Simulate scenarios to answer "what if" questions with your decision model.

In this hands-on workshop designed for operations researchers (decision scientists), developers, and data scientists, participants will get a guided introduction to DecisionOps via the Nextmv platform.

In this step-by-step video, we’ll walk you through deploying a Python OR-Tools traveling salesperson problem (TSP) model using the Nextmv Python template.

What is HiGHS? How is it used for MIP solving? And how can you accelerate the impact of decision models that use open source projects? We’ll cover all of this with a live walkthrough, demo, and a Q&A with the HiGHS project maintainers.