Neural Search - From Prototype to Production with Jina: Build deep learning–powered search systems that you can deploy and manage with ease

Neural Search - From Prototype to Production with Jina: Build deep learning–powered search systems that you can deploy and manage with ease

Implement neural search systems on the cloud by leveraging Jina design patterns Key Features Identify the different search techniques and discover applications of neural search - Gain a solid understanding of vector representation and apply your knowledge in neural search - Unlock deeper levels of knowledge of Jina for neural search Book Description Search is a big and ever-growing part of the tech ecosystem. Traditional search, however, has limitations that are hard to overcome because of the way it is designed. Neural search is a novel approach that uses the power of machine learning to retrieve information using vector embeddings as first-class citizens, opening up new possibilities of improving the results obtained through traditional search. Although neural search is a powerful tool, it is new and finetuning it can be tedious as it requires you to understand the several components on which it relies. Jina fills this gap by providing an infrastructure that reduces the time and complexity involved in creating deep learning–powered search engines. This book will enable you to learn the fundamentals of neural networks for neural search, its strengths and weaknesses, as well as how to use Jina to build a search engine. With the help of step-by-step explanations, practical examples, and self-assessment questions, you'll become well-versed with the basics of neural search and core Jina concepts, and learn to apply this knowledge to build your own search engine. By the end of this deep learning book, you'll be able to make the most of Jina's neural search design patterns to build an end-to-end search solution for any modality. What you will learn Understand how neural search and legacy search work - Grasp the machine learning and math fundamentals needed for neural search - Get to grips with the foundation of vector representation - Explore the basic components of Jina - Analyze search systems with different modalities - Uncover the capabilities of Jina with the help of practical examples Who this book is for If you are a machine learning, deep learning, or artificial intelligence engineer interested in building a search system of any kind (text, QA, image, audio, PDF, 3D models, or others) using modern software architecture, this book is for you. This book is perfect for Python engineers who are interested in building a search system of any kind using state-of-the-art deep learning techniques. Table of Contents Neural Networks for Neural Search - Introducing Foundations of Vector Representation - System Design and Engineering Challenges - Learning Jina's Basics - Multiple Search Modalities - Basic Practical Examples with Jina - Exploring Advanced Use Cases of Jina "Neural Search - From Prototype to Production with Jina" is a must-have starter pack to understand neural search and how it works, learn the essential machine learning and math fundamentals for neural search, and get a great handle on the foundation of vector representation using the open source Jina AI framework. This book is a great tool for learning Jina AI, enabling its adopters to build deep learning search systems that can be designed, deployed, and managed with ease. It is packed with step-by-step explanations, practical examples, and self-assessment questions that will enhance. -- Ibrahim Haddad, Executive Director, LF AI & Data & PyTorch Foundation "This book combines a very practical guide to neural search with valuable explanations, an introduction to deep learning, and some real-world examples. Developers and machine learning engineers will learn the benefits of the Jina framework and discover its descriptive power for complex data processing architectures. The book demonstrates how to get from a simple search solution to a scalable multi-modal search solution with the smallest amount of custom coding." -- Karsten Schmidt, CTO AI/ML @SAP Bo Wang is a machine learning engineer at Jina AI. He has a background in computer science, especially interested in the field of information retrieval. In the past years, he has been conducting research and engineering work on search intent classification, search result diversification, content-based image retrieval, and neural information retrieval. At Jina AI, Bo is working on developing a platform for automatically improving search quality with deep learning. In his spare time, he likes to play with his cats, watch anime, and play mobile games. Cristian Mitroi is a machine learning engineer with a wide breadth of experience in full stack, from infrastructure to model iteration and deployment. His background is based in linguistics, which led to him focusing on NLP. He also enjoys, and has experience in, teaching and interacting with the community, and has given workshops at various events. In his spare time, he performs improv comedy and organizes too many pen-and-paper role-playing games. Feng Wang is a machine learning engineer at Jina AI. He received his Ph.D. from the department of computer