ML-Driven Search to Power Your Modern Data Strategy

• Titel: ML-Driven Search to Power Your Modern Data Strategy
• Verlagsort: [Erscheinungsort nicht ermittelbar]
• Verlag: O'Reilly Media, Inc.
• Jahr: 2023
• Umfang: 1 online resource
• Fussnoten: Machine-generated record
• ISBN: 978-1-0981-5626-8
• ISBN: 1-0981-5626-9
• Datenträger: Online-Ressource
• Sprache: und
• Sach-SW: Moteurs de recherche
• Sach-SW: Apprentissage automatique
• Sach-SW: Traitement automatique des langues naturelles
• Sach-SW: search engines
• K10plus-PPN: 1885007795
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4507220092
• Lokale URL Inst.: Zum Volltext

Abstract

When you look at operational analytics and business data analysis activities—such as log analytics, real-time application monitoring, website search, observability, and more—effective search functionality is key to identifying issues, improving customers experience, and increasing operational effectiveness. How can you support your business needs by leveraging ML-driven advancements in search relevance? In this report, authors Jon Handler, Milind Shyani, Karen Kilroy help executives and data scientists explore how ML can enable ecommerce firms to generate more pertinent search results to drive better sales. You'll learn how personalized search helps you quickly find relevant data within applications, websites, and data lake catalogs. You'll also discover how to locate the content available in CRM systems and document stores. With advancements in ML-driven search, businesses can realize even more benefits and improvements in their data and document search capabilities to better support their own business needs and the needs of their customers.