Introduction

This learning path is designed for technical developers (ie. Configuration Engineers) that are responsible for the creation and configuration of solutions using the Product Similarity Accelerator. The use of this Accelerator will provide the framework for building logics that use an optimization model for creating groupings of product similarities.

It will focus on the core and common tasks associated with developing Groovy logic and configuration of this module and highlight key components of the Product Similarity Accelerator for Configuration Engineers to build their technical comprehension for the configuration and customization of this Accelerator for customer requirements.

Learning Prerequisites

Make sure that you have these aspects covered before moving on to the course.

• Data Management

• Pricing Basics

• Pricing Terminology

• Product Similarity Accelerator Learning Path for All Audiences

• Product Similarity Accelerator Learning Path for Administrators

Learning Synopsis

This learning path will discuss the underlying architecture for product similarity, use of model classes, associated libraries, and the steps required to define a model, configure it, execute and generate the product similarity output results. It contains the foundational knowledge you need to thrive in your configuration of this Accelerator and expand your understanding of best practices associated with it.

You will also learn core technical concepts, use case customization, practice scenarios and all the details you need to implement solutions for multifactor price elasticity.

Learning Objectives

Core topics and takeaways:

• Demonstrate a comprehensive understanding of the underlying infrastructure that support this Accelerator.

• Illustrate the use of optimization model classes to create your product similarity model.

• Demonstrate the steps needed create custom logics and associate them with product similarity process.

• Understand the libraries needed for this product similarity accelerator.

• Illustrate the process for configuration of the product similarity model.

Learning Journey

This learning path is separated into sections that provide a basic roadmap for building your developer knowledge and understanding of the technical aspects of Product Similarity Accelerator.

The components in this path are designed to be experienced in a sequential order, as each subsequent level builds upon the knowledge acquired in the preceding learning path(s). This structured approach ensures that learners have the necessary foundation before progressing to more advanced concepts, optimizing your learning experience.

Product Similarity Accelerator Model

• Model Class and Library

  • Product Similarity Model Class

  • Product Similarity Library

• Model Definition

• Similarity Weighting

  • Data Aggregation

  • Text Transformation

  • Raw Similarity

  • Similar Products

  • User Inputs

  • Weighting Evaluation

• Product Similarity

  • Similarity Overview

  • Similarity Dashboard

  • Similarity Grouping

• Product Grouping

  • Create Grouping

  • Similarity Groups

  • Product Links by Groups

• Evaluations

Product Similarity Metrics

• Sentence Transformers

• Hamming Similarity

• Mahalanobis Similarity

• Weighted Average Similarity

Time commitment: For this path you should dedicate approximately 2 hours.