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
Details
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
Questions and feedback
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