Model Management and Vector Considerations in Elastic

In this chapter, we will provide an overview of the Hugging Face ecosystem, Elasticsearch’s Eland Python library, and practical strategies for using embedding models in Elasticsearch.

We will start by exploring the Hugging Face platform, discussing how to get started, selecting suitable models, and leveraging its vast collection of datasets. We will also delve into the features offered by Hugging Face’s Spaces and how to use them effectively.

Then, we will introduce the Eland Python library, created by Elastic, and demonstrate its usage through a Jupyter Notebook example.

The topics that we will cover in this chapter are as follows:

• Eland Python library created by Elastic
• Index mappings
• Machine Learning (ML) nodes
• Integrating ML models into Elasticsearch
• Critical aspects of planning for cluster capacity
• Storage efficiency strategies that can help optimize the performance and resource...