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Enabling Machine Learning Operations

Updated on July 5, 2022

Enable Machine Learning Operations (MLOps) so that you can replace active models in predictions with other models, scorecards, or fields, and then deploy the candidate models to production. To enable MLOps, update Prediction Studio settings with the work queue for data scientists, the analytics repository, and optionally, an email account for sending notifications.

  1. In the navigation pane of Prediction Studio, click SettingsPrediction Studio settings.
  2. Optional: If you want to use a different work queue for data scientists than the default work queue DataScientistWorkqueue, in the Work queue field, enter the name of your work queue.
    For more information, see Creating a work queue.
  3. Add your data scientists' operator IDs to the work group that is associated with the work queue configured in the Work queue field.
    Note: For example, the default work queue DataScientistWorkqueue is configured with the Default work group. To received work related to model updates, the operator must be a part of the Default work group.

    For more information, see Defining work routing settings for an operator.

    Work queue configuration in Prediction Studio settings
    In the Prediction Studio settings, the default data scientist work queue is selected.
  4. In the Storage section, in the Analytics repository field, enter the name of the analytics repository for storing machine learning models.
    Analytics repository configuration in Prediction Studio settings
    In the Prediction Studio settings, the AWS analytics repository is selected.
  5. Click Save.
What to do next: If you want data scientists to get email notifications from Prediction Studio about performance issues and important changes concerning models and predictions, enable email notifications. For more information, see Enabling Prediction Studio email notifications.
  • Previous topic Analyzing example projects and models in Prediction Studio
  • Next topic Configuring the monitoring of model input and output

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