Managing Your Coveo ML Models

Managing Your Coveo ML Models

Learn to manage the machine learning models in your Coveo organization in this quick and easy course.

About this course

 

Coveo ML provides a wide array of model options to improve search result relevancy, power query suggestions, provide content recommendations, and drive dynamic navigation to ensure that people who are using your web site or application are finding what they need next.

This course shows how to create and configure those models in the Coveo Cloud V2 Console’s Machine Learning Models panel, and how to associate them with your query pipelines so that they provide personalized experiences in all of your Coveo-driven sites and applications.

This course is primarily for platform administrators who are responsible for creation and maintenance of query pipelines and their associated features. This information is also useful for other audiences to understand, including developers who develop the user interfaces which will benefit from the ML models, analysts who are responsible for evaluating success metrics and recommending adjustments, and business stakeholders who may wish to understand the available options when working with analysts to determine appropriate optimizations.

About this course

 

Coveo ML provides a wide array of model options to improve search result relevancy, power query suggestions, provide content recommendations, and drive dynamic navigation to ensure that people who are using your web site or application are finding what they need next.

This course shows how to create and configure those models in the Coveo Cloud V2 Console’s Machine Learning Models panel, and how to associate them with your query pipelines so that they provide personalized experiences in all of your Coveo-driven sites and applications.

This course is primarily for platform administrators who are responsible for creation and maintenance of query pipelines and their associated features. This information is also useful for other audiences to understand, including developers who develop the user interfaces which will benefit from the ML models, analysts who are responsible for evaluating success metrics and recommending adjustments, and business stakeholders who may wish to understand the available options when working with analysts to determine appropriate optimizations.