Vector Search For Practitioners With Elastic

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Vector Search For Practitioners With Elastic

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Author : Bahaaldine Azarmi
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-11-30

Vector Search For Practitioners With Elastic written by Bahaaldine Azarmi and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-30 with Computers categories.

"This book delves into the practical applications of vector search in Elastic and embodies a broader philosophy. It underscores the importance of search in the age of Generative Al and Large Language Models. This narrative goes beyond the 'how' to address the 'why' - highlighting our belief in the transformative power of search and our dedication to pushing boundaries to meet and exceed customer expectations." Shay Banon Founder & CTO at Elastic Key Features Install, configure, and optimize the ChatGPT-Elasticsearch plugin with a focus on vector data Learn how to load transformer models, generate vectors, and implement vector search with Elastic Develop a practical understanding of vector search, including a review of current vector databases Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionWhile 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.What you will learn Optimize performance by harnessing the capabilities of vector search Explore image vector search and its applications Detect and mask personally identifiable information Implement log prediction for next-generation observability Use vector-based bot detection for cybersecurity Visualize the vector space and explore Search.Next with Elastic Implement a RAG-enhanced application using Streamlit Who this book is for If you're a data professional with experience in Elastic observability, search, or cybersecurity and are looking to expand your knowledge of vector search, this book is for you. This book provides practical knowledge useful for search application owners, product managers, observability platform owners, and security operations center professionals. Experience in Python, using machine learning models, and data management will help you get the most out of this book.

Elastic Stack 8 X Cookbook

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Author : Huage Chen
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-06-28

Elastic Stack 8 X Cookbook written by Huage Chen and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-28 with Computers categories.

Unlock the full potential of Elastic Stack for search, analytics, security, and observability and manage substantial data workloads in both on-premise and cloud environments Key Features Explore the diverse capabilities of the Elastic Stack through a comprehensive set of recipes Build search applications, analyze your data, and observe cloud-native applications Harness powerful machine learning and AI features to create data science and search applications Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionLearn how to make the most of the Elastic Stack (ELK Stack) products—including Elasticsearch, Kibana, Elastic Agent, and Logstash—to take data reliably and securely from any source, in any format, and then search, analyze, and visualize it in real-time. This cookbook takes a practical approach to unlocking the full potential of Elastic Stack through detailed recipes step by step. Starting with installing and ingesting data using Elastic Agent and Beats, this book guides you through data transformation and enrichment with various Elastic components and explores the latest advancements in search applications, including semantic search and Generative AI. You'll then visualize and explore your data and create dashboards using Kibana. As you progress, you'll advance your skills with machine learning for data science, get to grips with natural language processing, and discover the power of vector search. The book covers Elastic Observability use cases for log, infrastructure, and synthetics monitoring, along with essential strategies for securing the Elastic Stack. Finally, you'll gain expertise in Elastic Stack operations to effectively monitor and manage your system.What you will learn Discover techniques for collecting data from diverse sources Visualize data and create dashboards using Kibana to extract business insights Explore machine learning, vector search, and AI capabilities of Elastic Stack Handle data transformation and data formatting Build search solutions from the ingested data Leverage data science tools for in-depth data exploration Monitor and manage your system with Elastic Stack Who this book is for This book is for Elastic Stack users, developers, observability practitioners, and data professionals ranging from beginner to expert level. If you’re a developer, you’ll benefit from the easy-to-follow recipes for using APIs and features to build powerful applications, and if you’re an observability practitioner, this book will help you with use cases covering APM, Kubernetes, and cloud monitoring. For data engineers and AI enthusiasts, the book covers dedicated recipes on vector search and machine learning. No prior knowledge of the Elastic Stack is required.

Getting Started With Duckdb

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Author : Simon Aubury
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-06-24

Getting Started With Duckdb written by Simon Aubury and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-24 with Computers categories.

Analyze and transform data efficiently with DuckDB, a versatile, modern, in-process SQL database Key Features Use DuckDB to rapidly load, transform, and query data across a range of sources and formats Gain practical experience using SQL, Python, and R to effectively analyze data Learn how open source tools and cloud services in the broader data ecosystem complement DuckDB’s versatile capabilities Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDuckDB is a fast in-process analytical database. Getting Started with DuckDB offers a practical overview of its usage. You'll learn to load, transform, and query various data formats, including CSV, JSON, and Parquet. The book covers DuckDB's optimizations, SQL enhancements, and extensions for specialized applications. Working with examples in SQL, Python, and R, you'll explore analyzing public datasets and discover tools enhancing DuckDB workflows. This guide suits both experienced and new data practitioners, quickly equipping you to apply DuckDB's capabilities in analytical projects. You'll gain proficiency in using DuckDB for diverse tasks, enabling effective integration into your data workflows.What you will learn Understand the properties and applications of a columnar in-process database Use SQL to load, transform, and query a range of data formats Discover DuckDB's rich extensions and learn how to apply them Use nested data types to model semi-structured data and extract and model JSON data Integrate DuckDB into your Python and R analytical workflows Effectively leverage DuckDB's convenient SQL enhancements Explore the wider ecosystem and pathways for building DuckDB-powered data applications Who this book is for If you’re interested in expanding your analytical toolkit, this book is for you. It will be particularly valuable for data analysts wanting to rapidly explore and query complex data, data and software engineers looking for a lean and versatile data processing tool, along with data scientists needing a scalable data manipulation library that integrates seamlessly with Python and R. You will get the most from this book if you have some familiarity with SQL and foundational database concepts, as well as exposure to a programming language such as Python or R.

Machine Learning With The Elastic Stack

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Author : Rich Collier
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-01-31

Machine Learning With The Elastic Stack written by Rich Collier and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-31 with Computers categories.

Leverage Elastic Stack’s machine learning features to gain valuable insight from your data Key FeaturesCombine machine learning with the analytic capabilities of Elastic StackAnalyze large volumes of search data and gain actionable insight from themUse external analytical tools with your Elastic Stack to improve its performanceBook Description Machine Learning with the Elastic Stack is a comprehensive overview of the embedded commercial features of anomaly detection and forecasting. The book starts with installing and setting up Elastic Stack. You will perform time series analysis on varied kinds of data, such as log files, network flows, application metrics, and financial data. As you progress through the chapters, you will deploy machine learning within the Elastic Stack for logging, security, and metrics. In the concluding chapters, you will see how machine learning jobs can be automatically distributed and managed across the Elasticsearch cluster and made resilient to failure. By the end of this book, you will understand the performance aspects of incorporating machine learning within the Elastic ecosystem and create anomaly detection jobs and view results from Kibana directly. What you will learnInstall the Elastic Stack to use machine learning featuresUnderstand how Elastic machine learning is used to detect a variety of anomaly typesApply effective anomaly detection to IT operations and security analyticsLeverage the output of Elastic machine learning in custom views, dashboards, and proactive alertingCombine your created jobs to correlate anomalies of different layers of infrastructureLearn various tips and tricks to get the most out of Elastic machine learningWho this book is for If you are a data professional eager to gain insight on Elasticsearch data without having to rely on a machine learning specialist or custom development, Machine Learning with the Elastic Stack is for you. Those looking to integrate machine learning within their search and analytics applications will also find this book very useful. Prior experience with the Elastic Stack is needed to get the most out of this book.

The Hundred Page Machine Learning Book

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Author : Andriy Burkov
language : en
Publisher:
Release Date : 2019

The Hundred Page Machine Learning Book written by Andriy Burkov and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Machine learning categories.

Provides a practical guide to get started and execute on machine learning within a few days without necessarily knowing much about machine learning.The first five chapters are enough to get you started and the next few chapters provide you a good feel of more advanced topics to pursue.

Scaling Up Machine Learning

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Author : Ron Bekkerman
language : en
Publisher: Cambridge University Press
Release Date : 2012

Scaling Up Machine Learning written by Ron Bekkerman and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Computers categories.

This integrated collection covers a range of parallelization platforms, concurrent programming frameworks and machine learning settings, with case studies.

Ai

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Author : さしみもち
language : ja
Publisher: インプレス NextPublishing
Release Date : 2025-03-07

Ai written by さしみもち and has been published by インプレス NextPublishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-07 with categories.

検索技術は進化を続けています。キーワード検索からベクトル検索、さらに生成AIと組み合わせたRAG（Retrieval-Augmented Generation）まで、その可能性は広がるばかりです。本書は、全文検索エンジン「Apache Solr」を活用し、次世代の検索システムを構築するための実践的なガイドです。 従来のキーワード検索では難しかった「ニュアンス検索」「類似画像検索」「レコメンド機能」などを可能にするベクトル検索の仕組みを解説し、Solr上での環境構築や高度なチューニング手法を紹介します。また、生成AIとベクトル検索を組み合わせたRAGシステムの開発方法についても詳しく解説し、検索技術の最新トレンドを実務で活かすためのノウハウを提供します。 Solrを活用した検索システムの構築・運用を行うエンジニアはもちろん、AIや機械学習の知識を活かした検索技術を学びたい方にも最適な一冊です。Solr × ベクトル検索の最前線を、この本でしっかりと学びましょう！ 【目次】 第1章 全文検索エンジンApache Solr 第2章 全文検索エンジンとしてのSolr 第3章 ベクトル検索の理論と要素技術 第4章 Solr上でベクトル検索を動かす 第5章 実データを使ったベクトル検索 第6章 生成AIとベクトル検索 第7章 SolrでRAGシステムを構築する 付録A 本書で扱うモデル 付録B フレームワークとサービス 付録C Hierarchical Navigate Small World 付録D もっと活用Solr

Machine Learning Engineering In Action

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Author : Ben Wilson
language : en
Publisher: Simon and Schuster
Release Date : 2022-05-17

Machine Learning Engineering In Action written by Ben Wilson and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-17 with Computers categories.

Field-tested tips, tricks, and design patterns for building machine learning projects that are deployable, maintainable, and secure from concept to production. In Machine Learning Engineering in Action, you will learn: Evaluating data science problems to find the most effective solution Scoping a machine learning project for usage expectations and budget Process techniques that minimize wasted effort and speed up production Assessing a project using standardized prototyping work and statistical validation Choosing the right technologies and tools for your project Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices Ferrying a machine learning project from your data science team to your end users is no easy task. Machine Learning Engineering in Action will help you make it simple. Inside, you'll find fantastic advice from veteran industry expert Ben Wilson, Principal Resident Solutions Architect at Databricks. Ben introduces his personal toolbox of techniques for building deployable and maintainable production machine learning systems. You'll learn the importance of Agile methodologies for fast prototyping and conferring with stakeholders, while developing a new appreciation for the importance of planning. Adopting well-established software development standards will help you deliver better code management, and make it easier to test, scale, and even reuse your machine learning code. Every method is explained in a friendly, peer-to-peer style and illustrated with production-ready source code. About the technology Deliver maximum performance from your models and data. This collection of reproducible techniques will help you build stable data pipelines, efficient application workflows, and maintainable models every time. Based on decades of good software engineering practice, machine learning engineering ensures your ML systems are resilient, adaptable, and perform in production. About the book Machine Learning Engineering in Action teaches you core principles and practices for designing, building, and delivering successful machine learning projects. You'll discover software engineering techniques like conducting experiments on your prototypes and implementing modular design that result in resilient architectures and consistent cross-team communication. Based on the author's extensive experience, every method in this book has been used to solve real-world projects. What's inside Scoping a machine learning project for usage expectations and budget Choosing the right technologies for your design Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices About the reader For data scientists who know machine learning and the basics of object-oriented programming. About the author Ben Wilson is Principal Resident Solutions Architect at Databricks, where he developed the Databricks Labs AutoML project, and is an MLflow committer.

An Introduction To Composite Materials

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Author : D. Hull
language : en
Publisher: Cambridge University Press
Release Date : 1996-08-13

An Introduction To Composite Materials written by D. Hull and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996-08-13 with Technology & Engineering categories.

This edition has been greatly enlarged and updated to provide both scientists and engineers with a clear and comprehensive understanding of composite materials. In describing both theoretical and practical aspects of their production, properties and usage, the book crosses the borders of many disciplines. Topics covered include: fibres, matrices, laminates and interfaces; elastic deformation, stress and strain, strength, fatigue crack propagation and creep resistance; toughness and thermal properties; fatigue and deterioration under environmental conditions; fabrication and applications. Coverage has been increased to include polymeric, metallic and ceramic matrices and reinforcement in the form of long fibres, short fibres and particles. Designed primarily as a teaching text for final-year undergraduates in materials science and engineering, this book will also interest undergraduates and postgraduates in chemistry, physics, and mechanical engineering. In addition, it will be an excellent source book for academic and technological researchers on materials.

Vector And Tensor Analysis

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Author : Louis Brand
language : en
Publisher:
Release Date : 1947

Vector And Tensor Analysis written by Louis Brand and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1947 with Calculus of tensors categories.