MOS, an application to help you deploy mathematical optimization models
We built MOS in response to the frictions we experienced in deploying optimization solutions. Some of the key benefits provided are the following: - Models can be easily uploaded to the application after adding simple annotations to the model code. - Models can be accessed via various available interfaces, including a REST API, a web graphical user interface, and client libraries in popular programming languages such as Python and Julia. - Models can be run with different inputs by workers running locally or distributed over the network. - Intermediate and end results can be extracted,…
In plain words
MOS is a deployment platform for mathematical optimization models that simplifies the process of putting optimization solutions into production. Users annotate their model code and upload it to access via REST API, web interface, or client libraries in Python and Julia. The application supports running models locally or across distributed networks with different inputs, and allows users to extract, browse, and analyze results without writing custom code.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
We built MOS in response to the frictions we experienced in deploying optimization solutions. Some of the key benefits provided are the following: - Models can be easily uploaded to the application after adding simple annotations to the model code. - Models can be accessed via various available interfaces, including a REST API, a web graphical user interface, and client libraries in popular programming languages such as Python and Julia. - Models can be run with different inputs by workers running locally or distributed over the network. - Intermediate and end results can be extracted, browsed, and analyzed. This is all available without the need for (the typically required) custom ad-hoc code.
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