Book Image

Natural Language Processing with AWS AI Services

By : Mona M, Premkumar Rangarajan
Book Image

Natural Language Processing with AWS AI Services

By: Mona M, Premkumar Rangarajan

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)
1
Section 1:Introduction to AWS AI NLP Services
5
Section 2: Using NLP to Accelerate Business Outcomes
15
Section 3: Improving NLP Models in Production

Section 3: Improving NLP Models in Production

In this section, we will dive deep into some strategies for how to include a human in the loop for different purposes in a document processing workflow and cover best practices on how to autoscale your NLP inference for enterprise traffic.

This section comprises the following chapters:

  • 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