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Vector Search for Practitioners with Elastic. A toolkit for building NLP solutions for search, observability, and security using vector search

Język publikacji: angielskim
Autorzy:
Bahaaldine Azarmi, Jeff Vestal, Shay Banon
Vector Search for Practitioners with Elastic. A toolkit for building NLP solutions for search, observability, and security using vector search Bahaaldine Azarmi, Jeff Vestal, Shay Banon - okladka książki

Vector Search for Practitioners with Elastic. A toolkit for building NLP solutions for search, observability, and security using vector search Bahaaldine Azarmi, Jeff Vestal, Shay Banon - okladka książki

Serie wydawnicze:
Hands-on
Wydawnictwo:
Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
240
Dostępne formaty:
     PDF
     ePub

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Do przechowalni

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.
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O autorach książki

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.
Jeff is holding a solutions engineer role in the US team at Elastic. He has more than 5 years of experience working at Elastic with more than 20 years of overall experience. He led the initiative to set up elastic Machine Learning at E*TRADE and created an elastic slack bot integration. He recently moved back to Chicago after a year in Georgia and enjoys woodworking and spending time with his wife, kids, and friends outside of work.
Founder and CTO at Elastic

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