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

Summary

In this chapter, we learned how to build an auditing workflow for named entity recognition to solve real-world challenges that many organizations face today with document processing, using Amazon Textract, Amazon Comprehend, and Amazon A2I. We reviewed the loan authentication use case to validate the documents before they can be passed to a loan processor. We considered an architecture based on conditions such as reducing the validation time from 2 to 4 weeks to 24 hours within the first 3 months of solution implementation. We assumed that you, the reader, are the solution architect assigned to this project, and we reviewed an overview of the solution components along with an architectural illustration in Figure 4.1.

We then went through the pre-requisites for the solution build, set up an Amazon SageMaker Notebook instance, cloned our GitHub repository, and started executing the code in the notebook based on instructions from this chapter. We covered training an Amazon...