Practical Solutions for Modern NLP Challenges

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 LLMsfrom 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

  Cloud-based open-source LLM lifecycle covers model selection, fine-tuning, deployment, and maintenance, tools Practical Code Examples Step-by-step guides for sentiment analysis, text generation, and NER using AWS and open-source Practical Use-Cases: Explore domain-specific applications of LLMs in customer support, marketing, and content creation

  Autorentext

Anvesh currently serves as a VP, Sr Lead ML engineer (LLM) at JP Morgan Chase, specializing in NLP applications. With a fervent advocacy for data science and artificial intelligence, he boasts 11+ years in IT and 9 years of experience in the Analytics field executed predictive and prescriptive solutions. Holding a master's degree from Oklahoma State University, he majored in data mining, following his bachelor's in computer science from JNTU University in India. Originating from South India, he commenced his career as a Software Engineer, catering to esteemed Fortune 500 clients such as GE, Cisco, and Tech Mahindra. Additionally, he aided stakeholders in capitalizing on the true value of AI & ML using actionable data insights and was responsible for overseeing the design of ML.

Venkat Gunnu is a Senior Executive Director of Knowledge Management and Innovation at JPM Chase. He is an executive with a successful background crafting enterprise-wide data and data science solutions, GenAI, process improvements, and data and data science-centric products.

Shubham is a Software Engineer with expertise in machine learning, cloud technologies, and AI-powered solutions. I have experience developing and optimizing systems like Retrieval-Augmented Generation (RAG) models, integrating AI technologies like ChatGPT and Mistral for smarter, real-time information retrieval.

Jayanth is a seasoned Machine Learning Engineer with 12 years of experience, specializing in Python programming, large language models (LLM), ModelOps, and automation technologies. With a strong background in deploying and optimizing machine learning models, he excels in creating efficient workflows that streamline the model lifecycle from development to production.

Sundar Krishnan is seasoned Data Science leader with over 12 years of experience. As a Senior Manager at CVS Health, he oversees Data Science and Data Engineering teams, driving healthcare products to enhance member health outcomes.

Inhalt

Chapter 1: Introduction to LLMs, SLMs, and Modern NLP Challenges.- Chapter 2: Text Generation with LLMs and SLMs.- Chapter 3: Text Classification with LLMs and SLMs.- Chapter 4: Named Entity Recognition (NER) with LLMs and SLMs.- Chapter 5: Sentiment Analysis with LLMs and SLMs.- Chapter 6: Question Answering (QA).- Chapter 7: Text Summarization.- Chapter 8: Language Translation.- Chapter 9: Dialogue Systems.- Chapter 10: Text Correction and Language Modeling.- Chapter 11: Coreference Resolution and Text Entailment.- Chapter 12: Emerging Trends and Future Directions in NLP.