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 covered how you can use Amazon Textract to automate your existing documents. We introduced a fictional bank use case with the help of LiveRight Pvt Ltd. We showed you how using an architecture can help banks automate their loan origination process and set up compliance and control with Amazon Comprehend. We also covered code samples using a sample bank statement, and how you can extract data from the scanned bank statement and save it into a CSV.text file in Amazon S3 for further analysis. Then, we showed you how you can use Amazon Comprehend to detect PII using a sync API and how you can redact that sample bank data text/CSV in Amazon S3 using an Amazon Comprehend batch PII redaction job.

We then covered some architecture patterns for using real-time processing document workflows versus batch processing workflows. We also provided some GitHub implementations that can be used to process large-scale documents.

In this chapter, you learned the differences...