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

We have just scratched the surface with what we can do with written text with this use case – the possibilities are truly endless! With just a few steps, by leveraging the advanced AI capabilities offered by services such as Amazon Textract, and the serverless scalable visualization offered by Amazon QuickSight, we were able to create powerful visuals from content scribbled on a piece of paper.

We began by creating the SageMaker Jupyter notebook instance we needed for this solution, cloned the GitHub repository for this chapter, created an S3 bucket, and executed the steps in the notebook to format the QuickSight S3 manifest file. Then, we used Amazon Textract and the Textract Response Parser library to read the contents of the handwritten receipts before creating CSV files that were uploaded to the S3 bucket. We concluded the notebook after executing these steps and then logged into the AWS Management Console and registered to use Amazon QuickSight.

In QuickSight...