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Practical Solutions for Modern Nlp Challenges: Mastering Llms and Slms for Real-World Nlp in Cloud and Open-Source
Paperback
Technology & EngineeringNetworkingProbability & Statistics
Publisher Price: $44.99
Publisher: Apress
Published: Jan 3 2026
Pages: 539
Weight: 2.14
Height: 1.15 Width: 7.00 Depth: 10.00
Language: English
Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP), enabling advanced applications such as machine translation, text summarization, and sentiment analysis. This book serves as a comprehensive guide for data scientists, machine learning engineers, and developers, offering foundational theory and practical skills to harness the power of LLMs for real-world problems. From understanding the fundamentals of LLMs to deploying them in cloud and open-source environments, this book equips readers with the essential knowledge to excel in modern NLP.
The book takes a hands-on approach, guiding readers through the end-to-end deployment of LLMs--from data collection and preprocessing to model training, evaluation, and real-time inference. Using popular frameworks like Amazon SageMaker and Hugging Face Transformers, you'll explore practical tasks such as text generation, classification, and named entity recognition. Additionally, it delves into industry use cases like customer support chatbots and content generation while addressing emerging trends, scaling techniques, and ethical considerations like bias and fairness in AI. This is your ultimate resource for mastering LLMs in production-ready environments.
You Will:
- Learn to implement cutting-edge NLP tasks such as text generation, sentiment analysis, and named entity recognition using AWS services and open-source tools like Hugging Face.
- Understand best practices for scaling and maintaining NLP models in production, focusing on real-time performance, monitoring, and iterative improvements.
- Practice techniques for training and optimizing LLMs, covering data preprocessing, hyperparameter tuning, and evaluation strategies.
This book is for:
Data scientists, Machine learning engineers, and developers
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