Mastering Machine Learning Search Applications
Advanced Techniques for Building Intelligent, Scalable, and Efficient Search Engines
Created by ChrisElevate your search applications to new heights with cutting-edge machine learning techniques. This comprehensive guide delves deep into the intricacies of building intelligent, scalable, and highly efficient search engines. You'll master advanced concepts such as vector space models, BERT embeddings, and transformer architectures, while learning to implement sophisticated NLP techniques and optimize complex ML algorithms. Designed for experienced software engineers and ML practitioners, this book offers a practical, hands-on approach to developing custom ML models, enhancing data pipelines, and automating ML workflows specifically for search applications. You'll explore the latest advancements in semantic search, query understanding, and personalization, equipping you with the skills to create search experiences that are both powerful and intuitive. From distributed indexing to MLOps best practices, this guide covers every aspect of building production-ready ML search applications. You'll learn how to leverage techniques like federated learning and transfer learning to improve your models, implement attention mechanisms for better relevance, and utilize knowledge graphs for enhanced entity recognition. By the end of this book, you'll have the expertise to architect, develop, and deploy sophisticated ML-powered search solutions that push the boundaries of what's possible in information retrieval.
Est. Length
315 pages (16 chapters)
Language
English
Publication date
Mar 23, 2025
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