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180 Embedding model evaluation & selection

Unit overview

Embedding models form a cornerstone of modern retrieval systems. Recent developments and subsequent proliferation of embedding models have greatly improved their capabilities. But this also makes model selection a very challenging task with a vast set of ever-expanding options.

This module will tackle how to navigate this landscape, and teach skills to screen, evaluate and select models.

Prerequisites

  • None

Learning objectives

  What are these?
  Each unit includes a set of Learning Goals and Learning Outcomes which form the unit's guiding principles.
  • Learning Goals describe the unit's key topics and ideas.
  • Learning Outcomes on the other hand describe tangible skills that the learner should be able to demonstrate

  Here, we will cover:

Learning Goals
  • The practical impact of embedding model selection on AI application performance
  • A systematic, evidence-based approach to embedding model selection
  • Skills to evaluate, implement, and maintain embedding models in production systems

  By the time you are finished, you will be able to:

Learning Outcomes
  • Evaluate embedding models based on quality, performance, cost, and resource requirements
  • Apply selection framework to identify, screen, and select embedding models
  • Design and implement effective evaluation strategies
  • Articulate key monitoring and maintenance needs for embedding models
  • Optimize embedding model selection for domain-specific applications

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