Manage Machine Learning Models

Coveo Machine Learning (Coveo ML) models are algorithms which leverage usage analytics data to provide contextually relevant recommendations (see Coveo Machine Learning Models).

The Models page of the Coveo Administration Console allows users with the required privileges to manage and review the ML models of a Coveo organization.

Creating a Model

Each Coveo ML feature has its own set of prerequisites and a different model creation procedure. Click one of the following links to access the corresponding procedure:

Creating a Model With JSON

Click one of the following links to access the corresponding procedure:

Editing a Model


"Status" Column

On the Models page of the Administration Console, the Status column indicates the current state of your Coveo ML models.

When inspecting the Status column, a given model is either in the Active or Inactive state. Additional information can be displayed depending on the model’s current state.

status column

The following table lists the possible model statuses, their definitions, and their status colors as shown in the Administration Console:

Status Definition Status Color
Active The model is active and available.
Inactive The model isn't available.
Update in queue Waiting to process a scheduled update or configuration change.
Updating The model is being rebuilt based on a new configuration.
Waiting The model is in the building queue.
Building The model is currently being processed.
Degraded The model is active, but has some limitations. Additional information is available in the Error section of a model (see Review Coveo Machine Learning Model Information).
Failed The model couldn't be built with the requested configuration. Additional information is available in the Error section of a model (see Review Coveo Machine Learning Model Information).
Update failed The model couldn't be updated with the requested configuration.
Error An error prevented the model from being built successfully.

"Learning Interval" Section

In this section, you can modify the following:

  • Frequency: The rate at which the model is retrained.

  • Data period: The usage analytics data time interval on which the model will be based.

  • The more visits you have on your page, the higher the Frequency should be.

  • The more hubs, interfaces, and languages you have, the longer the Data period should be.

  • Consider selecting a longer Data period when your search interface serves a small number of queries, and you want to improve the relevancy of recommendations by using a larger data set.

  • Consider selecting a shorter Data period when your search hub serves many queries, and you want the recommendations to be more responsive to trends in user behavior.

For more information on Coveo ML data learning behavior, see Training and Retraining.

"Learn From" Section

The Learn From section allows you to refine the data that the model uses to make its recommendations. By narrowing down the set of data that a model uses, you can better customize relevancy for specific user groups and use cases. You can apply filters on all events, or on every event that belongs to a specific category (i.e., search, click, view, or custom events).

  • You want your QS model to return queries that pertain to a specific user group, so you add a data filter to ensure that only a specific set of analytics are used by the model for training purposes.

  • Your Community search and Agent search have very specific vocabulary. You don’t want them to influence one another in the ART model learning process, so you add a filter on the Origin 1 (Page/Hub) dimension.

To add a filter:

  1. Click Add-Filter.

  2. In the Select a dimension drop-down menu, select the dimension on which you want to base the learning of the model.

  3. In the Select an operator drop-down menu, select the appropriate operator.

  4. In the Select value(s) drop-down menu, add, type, or select the appropriate value.

  5. Click Add Filter.

Required Privileges

By default, members of the Administrators and Relevance Managers built-in groups can view and edit elements of the Models page.

The following table indicates the privileges required to use elements of the Models page and associated panels (see Manage Privileges and Privilege Reference).

Action Service - Domain Required access level

View models

Machine Learning - Models

Search - Query pipelines


Edit models

Machine Learning - Models


Search - Query pipelines


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