Coveo Machine Learning


This course will provide you with in-depth information on how to set-up, maintain and analyze Coveo Machine Learning models to create relevant experiences for your audiences.

Learn the core principles, mechanics and models you need to master in order to fully leverage all Coveo Machine Learning features across Coveo platform use cases.

Element covered include:

  • - How to explain the fundamental concepts behind the Machine Learning
  • - How Coveo Machine Learning Model improves relevance
  • - Data used by Coveo Machine Learning and how it is used by each Models
  • - Configuring a Coveo Machine Learning Model Training Phase
  • - The role of Filter Context and User Context in Coveo Machine Learning
  • - Configuring a Query Suggest Model and evaluate its key performance measures
  • - Configuring an Automatic Relevance Tuning Model and evaluate its key performance measures
 4 HOURS  250

  Virtual Class


Course Agenda

Day 1

Coveo Core concepts review

Description: In this mandatory step, you will explore the concepts and elements that the Coveo Platform uses to power a Machine Learning driven search experience. This includes Query Parameters, Usage Analytics Events and Dimensions, Context, Query Pipelines and more.

Machine Learning Mechanics

Description: In this section, you will learn about the mechanics through which the Machine Learning features can optimize the experience for your Visitors. You will explore concepts like: how does it learn, what are the key things to know about each model, what type of information should be used to develop the models and more.

Day 2

Design Principles

Description: In this section, you will learn the 4 axis of design when building a Machine Learning driven search experience. You will learn how to clearly define your target audiences in terms that can be leverage to create a Machine Learning driven experience and how to configure your Search Interface to measure your audience behavior and utilization of Machine Learning.

Use Case

Description: In this final section, you will put everything you have learned so far into practice by studying 3 archetypical Machine Learning driven search experiences. We will show how the design principles are applied in the context of:

- A Search Portal

- A agent Panel

- An E-commerce portal

Machine Learning Use Cases

Description: In this section, we will cover the basics of Machine Learning instrumentation in the context of providing relevance. We will analyze a business use case example and discuss the importance of dimensions, custom events and context to make Machine Learning successful.

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