Goal¶

Deploy models with a data pipeline to a production or production-like environment for final user acceptance.¶

Operationalize a model¶

After you have a set of models that perform well, you can operationalize them for other applications to consume. Depending on the business requirements, predictions are made either in real time or on a batch basis. To deploy models, you expose them with an open API interface. The interface enables the model to be easily consumed from various applications, such as:

• Online websites
• Spreadsheets
• Dashboards
• Line-of-business applications
• Back-end applications

For examples of model operationalization with an Azure Machine Learning web service, see Deploy an Azure Machine Learning web service. It is a best practice to build telemetry and monitoring into the production model and the data pipeline that you deploy. This practice helps with subsequent system status reporting and troubleshooting.