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Step 0: Orient yourself to DominoStep 1: Create a projectStep 2: Configure your projectStep 3: Start a workspaceStep 4: Get your files and dataStep 5: Develop your modelStep 6: Clean up WorkspacesStep 7: Deploy your model
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Step 0: Orient yourself to Domino (R Tutorial)Step 1: Create a projectStep 2: Configure your projectStep 3: Start a workspaceStep 4: Get your files and dataStep 5: Develop your modelStep 6: Clean up WorkspacesStep 7: Deploy your model
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Model Monitoring and Remediation
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Monitor Workflows

Monitor Workflows

In Domino, you can use monitor models deployed on Domino and elsewhere. For Domino models deployed as Model APIs, you can use our Model API monitoring workflow, so you don’t have to maintain access to external data sources. For models deployed in other formats (such as an App, Launcher, or Job) or external to Domino, you can use our Model Monitor workflow.

Use Domino’s Model Monitor to see a single list of monitored models no matter how they are deployed.

Model API monitoring

When a model is deployed on Domino as a Model API, Domino:

  • Analyzes the training data to extract the model schema (if you register a Domino Training Set.

  • Captures predictions as Domino datasets for monitoring.

  • Generates drift detection and model quality analysis on a schedule (if you share the ground truth dataset with Domino), and alerts you if any thresholds are exceeded.

  • Allows you to easily reproduce the environment with access to the captured predictions to diagnose and fix issues with your model.

See Set up monitoring for Model APIs.

If you do not want Domino to manage the prediction data collection, use the Model Monitor to configure monitoring, even for Model APIs.

Model Monitor

For models deployed as other assets on Domino (App, Launcher, or Job) or external to Domino, you can use Domino to:

  • Connect to the data source where the training, prediction, and ground truth data reside.

  • Register a model’s entry along with its schema.

  • Set up drift detection and model quality monitoring by registering the location of every new batch of prediction or ground truth data.

  • Set up a schedule for Domino to run drift and model quality checks periodically and alert you if thresholds are exceeded.

See Set up Model Monitor.

Contact your Domino support person if you need more help in determining the best workflow.

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