Natural Language Processing with AWS AI Services
By :
Natural Language Processing with AWS AI Services
By:
Overview of this book
Natural language processing (NLP) uses machine learning to extract information from unstructured data. This book will help you to move quickly from business questions to high-performance models in production.
To start with, you'll understand the importance of NLP in today’s business applications and learn the features of Amazon Comprehend and Amazon Textract to build NLP models using Python and Jupyter Notebooks. The book then shows you how to integrate AI in applications for accelerating business outcomes with just a few lines of code. Throughout the book, you'll cover use cases such as smart text search, setting up compliance and controls when processing confidential documents, real-time text analytics, and much more to understand various NLP scenarios. You'll deploy and monitor scalable NLP models in production for real-time and batch requirements. As you advance, you'll explore strategies for including humans in the loop for different purposes in a document processing workflow. Moreover, you'll learn best practices for auto-scaling your NLP inference for enterprise traffic.
Whether you're new to ML or an experienced practitioner, by the end of this NLP book, you'll have the confidence to use AWS AI services to build powerful NLP applications.
Table of Contents (23 chapters)
Preface
Section 1:Introduction to AWS AI NLP Services
Free Chapter
Chapter 1: NLP in the Business Context and Introduction to AWS AI Services
Chapter 2: Introducing Amazon Textract
Chapter 3: Introducing Amazon Comprehend
Section 2: Using NLP to Accelerate Business Outcomes
Chapter 4: Automating Document Processing Workflows
Chapter 5: Creating NLP Search
Chapter 6: Using NLP to Improve Customer Service Efficiency
Chapter 7: Understanding the Voice of Your Customer Analytics
Chapter 8: Leveraging NLP to Monetize Your Media Content
Chapter 9: Extracting Metadata from Financial Documents
Chapter 10: Reducing Localization Costs with Machine Translation
Chapter 11: Using Chatbots for Querying Documents
Chapter 12: AI and NLP in Healthcare
Section 3: Improving NLP Models in Production
Chapter 13: Improving the Accuracy of Document Processing Workflows
Chapter 14: Auditing Named Entity Recognition Workflows
Chapter 15: Classifying Documents and Setting up Human in the Loop for Active Learning
Chapter 16: Improving the Accuracy of PDF Batch Processing
Chapter 17: Visualizing Insights from Handwritten Content
Chapter 18: Building Secure, Reliable, and Efficient NLP Solutions
Other Books You May Enjoy
Customer Reviews