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Deployment

With MLOps, the goal is to make model deployment easy. Regardless of your role—a business analyst, data scientist, data engineer, or member of an Operations team— you can easily create a deployment in MLOps. Deploy models built in DataRobot and those written in various programming languages like Python and R.

The following sections describe how to deploy models to a production environment of your choice and use MLOps to monitor and manage those models.

Topic Describes...
Add deployments Deploying DataRobot models, custom inference models, and external models to MLOps.
Model Registry Creating model packages in the Model Registry to enable deploying, sharing, and archiving them.
MLOps agent Monitoring and managing deployments running in an external environment outside of DataRobot MLOps.
Custom Model Workshop Bringing your own pretrained models into DataRobot as custom inference models and deploying them to a centralized deployment hub.
Prediction environments Setting up prediction environments on your own infrastructure, grouping prediction environments, and configuring permissions and approval workflows.
Algorithmia Using the Algorithmia platform to connect models to data sources and deploy them quickly to production. See the Algorithmia Developer Center for details.

Updated May 31, 2022
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