Deploy ML models to production to play an active role in making business decisions

Production deployment enables a model to play an active role in a business. Predictions from a deployed model can be used for business decisions.

Production platforms

There are various approaches and platforms to put models into production. Here are a few options:

Note

Prior to deployment, one has to insure the latency of model scoring is low enough to use in production.

Note

For deployment from Azure Machine Learning, see Deploy machine learning models to Azure.

A/B testing

When multiple models are in production, A/B testing may be used to compare model performance.

Next steps

Walkthroughs that demonstrate all the steps in the process for specific scenarios are also provided. They are listed and linked with thumbnail descriptions in the Example walkthroughs article. They illustrate how to combine cloud, on-premises tools, and services into a workflow or pipeline to create an intelligent application.