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  • Book Overview & Buying Vector Search for Practitioners with Elastic
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Vector Search for Practitioners with Elastic

Vector Search for Practitioners with Elastic

By : Bahaaldine Azarmi, Jeff Vestal, Vestal
4.9 (15)
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Vector Search for Practitioners with Elastic

Vector Search for Practitioners with Elastic

4.9 (15)
By: Bahaaldine Azarmi, Jeff Vestal, Vestal

Overview of this book

While natural language processing (NLP) is largely used in search use cases, this book aims to inspire you to start using vectors to overcome equally important domain challenges like observability and cybersecurity. The chapters focus mainly on integrating vector search with Elastic to enhance not only their search but also observability and cybersecurity capabilities. The book, which also features a foreword written by the founder of Elastic, begins by teaching you about NLP and the functionality of Elastic in NLP processes. Here you’ll delve into resource requirements and find out how vectors are stored in the dense-vector type along with specific page cache requirements for fast response times. As you advance, you’ll discover various tuning techniques and strategies to improve machine learning model deployment, including node scaling, configuration tuning, and load testing with Rally and Python. You’ll also cover techniques for vector search with images, fine-tuning models for improved performance, and the use of clip models for image similarity search in Elasticsearch. Finally, you’ll explore retrieval-augmented generation (RAG) and learn to integrate ChatGPT with Elasticsearch to leverage vectorized data, ELSER's capabilities, and RRF's refined search mechanism. By the end of this NLP book, you’ll have all the necessary skills needed to implement and optimize vector search in your projects with Elastic.
Table of Contents (17 chapters)
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Free Chapter
1
Part 1:Fundamentals of Vector Search
4
Part 2: Advanced Applications and Performance Optimization
7
Part 3: Specialized Use Cases
12
Part 4: Innovative Integrations and Future Directions

Overview of PII and redaction

PII refers to any data that can be used to identify an individual, either directly or indirectly, when combined with other information. PII includes data such as names, addresses, phone numbers, email addresses, Social Security numbers, driver’s license numbers, and credit card numbers. It is critical to protect PII due to privacy concerns, as well as legal and regulatory requirements that dictate how companies should manage and secure such data.

Redaction, in the context of data privacy, is the process of removing or obscuring sensitive information from documents, logs, and other data sources, so the remaining data can be shared or analyzed without exposing the PII. This involves techniques such as masking, pseudonymization, or encryption, depending on the context and requirements. The goal of redaction is to strike a balance between preserving the utility of the data and maintaining the privacy of the individuals involved.

Types of data...

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