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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