MLOps and predictions (V11.1)¶
July 17, 2025
The DataRobot V11.1.0 release includes many new features and capabilities, described below. See additional details of Release 11.1:
Predictions and MLOps¶
Pull an execution environment from an image URI¶
To add an execution environment, you can now provide a URI for an environment image published to an accessible container registry. Optionally, you can include the source archive used to build the image for reference purposes. This source archive is not used to build the environment.
When adding an environment as an image URI, URI filtering allows only the URIs defined for your organization. If the URI you provide isn't allowed, a warning appears as helper text. URI filtering is not enforced for API administrators.
Deployment governance workflow improvements¶
When governance management functionality is enabled for an organization, a setup checklist appears on the deployment's overview page. Users can click a setting tile in the checklist to open the relevant deployment setting page.
If a deployment is subject to a configured approval policy, the deployment is created as a draft deployment, as shown above. When a deployment is in the draft state, users can't make predictions, upload actuals or custom metric data, or create scheduled jobs. A deployment owner can request deployment approval on the deployment's overview page or the governance section of the deployment's activity log. After approval, the deployment is automatically moved out of the draft state and activated.
Pages in the activity log section now include a comments panel, where deployment owners and approvers can post comments to discuss deployment activity, configuration, and governance events. If a deployment is subject to an approval policy, the comments from the approval process are included in the comments panel.
Segmented analysis on the Challengers tab¶
On a deployment’s Challengers tab, users can now select a segment attribute and segment value to filter the challenger performance metrics charts.
View the Confusion Matrix in the Registry¶
For DataRobot and custom models in the Registry, the Insights tab now includes the Confusion Matrix insight. For more information, see the registered model insights documentation.
Batch prediction support for Alibaba Cloud MaxCompute¶
DataRobot now supports intake and write-back through a MaxCompute data connection when you configure a JDBC prediction source and destination with the Job Definitions UI or the Batch Prediction API. For a complete list of supported output options, see the data sources supported for batch predictions.
Streaming support in the moderation framework¶
There is now improved moderation support for streaming LLM chat completions. Chat completions now include datarobot_moderations when a deployment meets two requirements: the execution environment image includes the moderation library and the custom model code contains moderation_config.yaml. For streaming responses with moderation enabled, the first chunk now provides information about configured prompt guards and response guards.
List model support for custom models¶
Custom models now support the OpenAI client .models.list() method, which returns available models in a deployment along with basic information such as the owner and availability. This functionality is available out-of-the-box for managed RAGs, NIMs, and hosted LLMs. For custom models, you can customize the response by implementing the get_supported_llm_models() hook in custom.py.
Create custom model proxies for external models¶
In the Registry workshop, you can create custom model as a proxy for an external model. A proxy model contains proxy code created to connect with an external model, allowing you to use features like compliance documentation, challenger analysis, and custom model tests with a model running on infrastructure outside DataRobot. For more information, see the documentation.
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