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You're reading from  Vector Search for Practitioners with Elastic

Product typeBook
Published inNov 2023
PublisherPackt
ISBN-139781805121022
Edition1st Edition
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Authors (2):
Bahaaldine Azarmi
Bahaaldine Azarmi
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Bahaaldine Azarmi

Bahaaldine Azarmi, Global VP Customer Engineering at Elastic, guides companies as they leverage data architecture, distributed systems, machine learning, and generative AI. He leads the customer engineering team, focusing on cloud consumption, and is passionate about sharing knowledge to build and inspire a community skilled in AI.
Read more about Bahaaldine Azarmi

Jeff Vestal
Jeff Vestal
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Jeff Vestal

Jeff Vestal has a rich background spanning over a decade in financial trading firms and extensive experience with Elasticsearch. He offers a unique blend of operational acumen, engineering skills, and machine learning expertise. As a Principal Customer Enterprise Architect, he excels at crafting innovative solutions, leveraging Elasticsearch's advanced search capabilities, machine learning features, and generative AI integrations, adeptly guiding users to transform complex data challenges into actionable insights.
Read more about Jeff Vestal

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Seeing ELSER in action

In this part, we are going to walk you through how easy it is to get started with ELSER and see some significant results right out of the box. The first part will be to go through the required hardware. Then, we will look at preparing the index; finally, we are going to fire a couple of queries to illustrate the power of ELSER.

Hardware consideration

The ELSER documentation (https://www.elastic.co/guide/en/machine-learning/current/ml-nlp-elser.html#elser-hw-benchamrks) goes through benchmarks in representative data, which highlights KPIs such as inference, indexing, query, and latency.

You will see there that the hardware configuration significantly impacts the performance of ELSER. Here are some key takeaways to consider for sizing your infrastructure:

  • CPU and memory: The more powerful the ML node (in terms of CPU and memory), the better the performance. For instance, an ML node with 16 GB of memory and 8 vCPUs performs better than one with...
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Vector Search for Practitioners with Elastic
Published in: Nov 2023Publisher: PacktISBN-13: 9781805121022

Authors (2)

author image
Bahaaldine Azarmi

Bahaaldine Azarmi, Global VP Customer Engineering at Elastic, guides companies as they leverage data architecture, distributed systems, machine learning, and generative AI. He leads the customer engineering team, focusing on cloud consumption, and is passionate about sharing knowledge to build and inspire a community skilled in AI.
Read more about Bahaaldine Azarmi

author image
Jeff Vestal

Jeff Vestal has a rich background spanning over a decade in financial trading firms and extensive experience with Elasticsearch. He offers a unique blend of operational acumen, engineering skills, and machine learning expertise. As a Principal Customer Enterprise Architect, he excels at crafting innovative solutions, leveraging Elasticsearch's advanced search capabilities, machine learning features, and generative AI integrations, adeptly guiding users to transform complex data challenges into actionable insights.
Read more about Jeff Vestal