A Practical Guide for Companies Implementing Large AI Models

Aimed at business leaders and technical teams, this session introduces the preparation, development, and implementation processes for large-scale model projects, focusing on business scenarios, solution selection, and budget allocation.

author
Jia Liyang, Wang Qi
publishers
People's Post and Telecommunications Publishing House
Issue date
2025-06
AI大模型企业落地指南

Book Information

AI macromodelGuide to Enterprise Implementation; Jia Liyang, Wang Qi; People's Posts and Telecommunications Press; June 2025; ISBN 9787115661333

What problem does this book address?

When enterprises adopt large language models, the first questions they must address are: at which stage of the process will they be used, what data needs to be prepared, and how to determine whether the investment is worthwhile. The *Guide to Enterprise Implementation of Large Language Models* organizes its content around these questions, aligning business needs with technical implementation along a single project path.

Content Strategy

The book begins by covering the fundamentals of AI and large language models, as well as their commercial value, before discussing preparation for deployment, solution comparisons, and implementation processes. It then introduces model principles and application development. Key topics include the trade-offs between public cloud, external partnerships, fine-tuning, RAG, and in-house solutions, as well as budgeting, evaluation, and post-launch feedback. This is a thematic summary of the content and does not replace the publisher’s complete table of contents.

Who Is This Book For?

This guide is intended for business leaders, product managers, and IT teams looking to drive AI projects forward. Managers can start by reviewing the sections on use cases and budgeting; technical staff can refer to the implementation chapter to identify their team’s gaps in data, computing power, models, and talent.

Reasons for Recommendation and How to Read It

It serves as a good common starting point for project discussions. We recommend reading it with a specific business process in mind, listing the expected outcomes, required resources, and acceptance criteria separately, and then comparing the options—rather than choosing a model first and then looking for a use case.

The Boundaries of Reading

This book was published in 2025; product interfaces, model capabilities, and platform APIs are subject to change. The content in this book regarding record filing and copyright should be verified against current requirements and should not be used as a direct substitute for compliance assessments in specific projects.

Official and Reference Sources

Related Books

Deep Learning with Python (3rd Edition · Original English Edition)

Move from the fundamentals of deep learning to image, text, and generative AI, and build practical skills using Keras 3 and mainstream frameworks. Suitable for readers with a basic understanding of Python who want to systematically expand their knowledge of machine learning models.

AI 3.0

A must-read in the field of Artificial Intelligence, it will take you through the mysteries of Artificial Intelligence and trigger your infinite imagination of future technology.

The Underlying Logic of Artificial Intelligence

From search, perception, and learning to reasoning, this book establishes a knowledge framework for understanding artificial intelligence from a general education perspective, making it ideal for Chinese readers who wish to grasp the technical principles and limitations of the technology.

No comments

none
No comments...