Hugging Face in Action
Build Intelligent Applications with Transformers, Agents, and Rag
Published on 1. December 2025
Book
Paperback/Softback
368 pages
978-1-63343-671-8 (ISBN)
Description
AI libraries evolve weekly and tutorials rarely keep pace. Need a reliable playbook that simply works? Hugging Face in Action turns cutting-edge models into clear, runnable Python projects you can launch today.
Inside you'll find:
Utilizing Hugging Face Transformers and Pipelines for NLP tasks: Produce accurate NLP results without deep math or custom training.
Applying Hugging Face techniques for Computer Vision projects: Detect objects and classify images using pretrained models that save months.
Manipulating Hugging Face Datasets for efficient data handling: Share data efficiently, eliminating fragile, one-off scripts.
Training Machine Learning models with AutoTrain functionality: Train custom models with almost no code, accelerating experiments and proofs of concept.
Autonomous AI agents: Implement AI agents to automate tasks and integrate them into your applications.
Developing LLM-based applications using LangChain and LlamaIndex: Build retrieval-augmented chatbots that answer from your private knowledge bases.
Hugging Face in Action by Wei-Meng Lee delivers a step-by-step, project-based roadmap in print and eBook. Each chapter adds one layer of the modern Hugging Face ecosystem, reinforcing concepts through hands-on builds.
Clear tips, checklists, and complete code samples help you avoid pitfalls and stay productive.
You will start with simple text generation and progress to image classification, retrieval-augmented generation (RAG), and autonomous AI agents. Clear tips, checklists, and performance notes help you avoid common pitfalls while staying productive.
By the end of this book, you will be ready to fine-tune models, manage datasets, and release AI features. Your solutions will remain maintainable as libraries evolve.
Ideal for Python developers comfortable with NumPy or Pandas who want a fast, practical entry into applied AI.
Inside you'll find:
Utilizing Hugging Face Transformers and Pipelines for NLP tasks: Produce accurate NLP results without deep math or custom training.
Applying Hugging Face techniques for Computer Vision projects: Detect objects and classify images using pretrained models that save months.
Manipulating Hugging Face Datasets for efficient data handling: Share data efficiently, eliminating fragile, one-off scripts.
Training Machine Learning models with AutoTrain functionality: Train custom models with almost no code, accelerating experiments and proofs of concept.
Autonomous AI agents: Implement AI agents to automate tasks and integrate them into your applications.
Developing LLM-based applications using LangChain and LlamaIndex: Build retrieval-augmented chatbots that answer from your private knowledge bases.
Hugging Face in Action by Wei-Meng Lee delivers a step-by-step, project-based roadmap in print and eBook. Each chapter adds one layer of the modern Hugging Face ecosystem, reinforcing concepts through hands-on builds.
Clear tips, checklists, and complete code samples help you avoid pitfalls and stay productive.
You will start with simple text generation and progress to image classification, retrieval-augmented generation (RAG), and autonomous AI agents. Clear tips, checklists, and performance notes help you avoid common pitfalls while staying productive.
By the end of this book, you will be ready to fine-tune models, manage datasets, and release AI features. Your solutions will remain maintainable as libraries evolve.
Ideal for Python developers comfortable with NumPy or Pandas who want a fast, practical entry into applied AI.
More details
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 229 mm
Width: 186 mm
Thickness: 26 mm
Weight
492 gr
ISBN-13
978-1-63343-671-8 (9781633436718)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Wei-Meng Lee
Hugging Face in Action
Build intelligent applications with transformers, agents, and RAG
E-Book
11/2025
Simon + Schuster LLC
€49.44
Available for download
Person
Wei-Meng Lee is a technologist and educator known for turning emerging technologies into business-ready solutions. With decades of consulting and teaching experience, Wei-Meng distils complex AI workflows into step-by-step guides that help readers deliver value quickly.
Content
INTRODUCTION TO HUGGING FACE
GETTING STARTED
USING HUGGING FACE TRANSFORMERS AND PIPELINES FOR NLP TASKS
USING HUGGING FACE FOR COMPUTER VISION TASKS
EXPLORING, TOKENIZING, AND VISUALIZING HUGGING FACE DATASETS
FINE-TUNING PRE-TRAINED MODELS AND WORKING WITH MULTIMODAL MODELS
AGENTS
CREATING LLM-BASED APPLICATIONS USING LANGCHAIN AND LLAMAINDEX
BUILDING LANGCHAIN APPLICATIONS VISUALLY USING LANGFLOW
BUILDING WEB-BASED UI USING GRADIO
BUILDING LOCALLY-RUNNING LLM-BASED APPLICATIONS USING GPT4ALL
USING LLMS TO QUERY YOUR LOCAL DATA
GETTING STARTED
USING HUGGING FACE TRANSFORMERS AND PIPELINES FOR NLP TASKS
USING HUGGING FACE FOR COMPUTER VISION TASKS
EXPLORING, TOKENIZING, AND VISUALIZING HUGGING FACE DATASETS
FINE-TUNING PRE-TRAINED MODELS AND WORKING WITH MULTIMODAL MODELS
AGENTS
CREATING LLM-BASED APPLICATIONS USING LANGCHAIN AND LLAMAINDEX
BUILDING LANGCHAIN APPLICATIONS VISUALLY USING LANGFLOW
BUILDING WEB-BASED UI USING GRADIO
BUILDING LOCALLY-RUNNING LLM-BASED APPLICATIONS USING GPT4ALL
USING LLMS TO QUERY YOUR LOCAL DATA