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Vector Search for Practitioners with Elastic

You're reading from Vector Search for Practitioners with Elastic A toolkit for building NLP solutions for search, observability, and security using vector search

Product type Paperback

Published in Nov 2023

Publisher Packt

ISBN-13 9781805121022

Length 240 pages

Edition 1st Edition

Languages

Python

Tools

Streamlit

Concepts

Data Analysis

Authors (2):

Jeff Vestal

Bahaaldine Azarmi

View More author details

Table of Contents (17) Chapters

Preface

1. Part 1:Fundamentals of Vector Search

2. Chapter 1: Introduction to Vectors and Embeddings FREE CHAPTER

3. Chapter 2: Getting Started with Vector Search in Elastic

4. Part 2: Advanced Applications and Performance Optimization

5. Chapter 3: Model Management and Vector Considerations in Elastic

6. Chapter 4: Performance Tuning – Working with Data

7. Part 3: Specialized Use Cases

8. Chapter 5: Image Search

9. Chapter 6: Redacting Personal Identifiable Information Using Elasticsearch

10. Chapter 7: Next Generation of Observability Powered by Vectors

11. Chapter 8: The Power of Vectors and Embedding in Bolstering Cybersecurity

12. Part 4: Innovative Integrations and Future Directions

13. Chapter 9: Retrieval Augmented Generation with Elastic

14. Chapter 10: Building an Elastic Plugin for ChatGPT

15. Index

Why subscribe?

16. Other Books You May Enjoy

Part 3: Specialized Use Cases

This section delves into the specialized applications of vector search across various domains. From image search to the intricacies of cybersecurity, it explores how vector search is uniquely adapted to different contexts. These chapters provide a detailed perspective on the versatility of vector search, demonstrating its wide-ranging impact and utility in addressing specific, industry-related challenges.

This part has the following chapters:

Chapter 5, Image Search
Chapter 6, Redacting Personal Identifiable Information Using Elasticsearch
Chapter 7, Next Generation of Observability Powered by Vectors
Chapter 8, The Power of Vectors and Embedding in Bolstering Cybersecurity

The rest of the chapter is locked

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Authors (2)

Bahaaldine Azarmi

Bahaaldine Azarmi

Bahaaldine Azarmi, or Baha for short, is the head of solutions architecture in the EMEA South region at Elastic. Prior to this position, Baha co-founded ReachFive, a marketing data platform focused on user behavior and social analytics. He has also worked for a number of different software vendors, including Talend and Oracle, where he held positions as a solutions architect and architect. Prior to Machine Learning with the Elastic Stack, Baha authored books including Learning Kibana 5.0, Scalable Big Data Architecture, and Talend for Big Data. He is based in Paris and holds an MSc in computer science from Polytech'Paris.

See other products by Bahaaldine Azarmi

Vestal

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.

See other products by Vestal

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