Follow the full tutorial: Bring your custom Python decision model to Nextmv: Build, test, deploy
Now, with Nextmv’s language-based templates, you can develop and ship your custom Python decision models that use nearly any modeling tool or solver. Here are a few examples (in addition to the integrations listed above): Seeker , CVXPY, Python-MIP, PyOptInterface, Linopy, CPMpy, PuLP, and Clarabel.
When you bring your custom Python model to Nextmv you can validate model changes, simulate scenarios, share run details and experiment results with stakeholders, and create a system of record for your decision model. (You can even incorporate Prophet, Statsmodels, and scikit-learn to better bridge the ML and OR gap – check out this talk.)
How to deploy your custom Python decision model to Nextmv
Start with our Python template and insert your model code into the main.py file.
The above is a very basic “Hello world” model that demonstrates how simple it is to paste your custom model into the template so you view results in the Nextmv platform.
There are a just a few basic concepts required for bringing your Python decision model to our DecisionOps platform. The model must:
- Read from either stdin or a file
- Write results either to stdout or a file
- Write logs to stderr
- Format statistics in json to view in console
Tutorial using Python OR-Tools TSP
Let’s take a look at an OR-Tools example. 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.
Getting started
Sign up for a free account and start a free trial to create a custom app in Python.
Follow the full tutorial: Bring your custom Python decision model to Nextmv: Build, test, deploy