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

Understanding how to create a serverless pipeline for medical claims

In the previous sections, we covered the building blocks of the architecture by using the Amazon Textract Sync API, the Amazon Comprehend Medical Detect Entities Sync API, and Amazon SNS to send invalid claims. We defined functions for this workflow and called the text extraction and validation functions to showcase the use case or workflow with both a valid and invalid medical claim form. These functions can be moved into lambda code and, along with S3 event notifications, can be invoked to create a scalable pipeline for medical claims processing. We can do this by using the following architecture:

Figure 12.15 – Automating an architecture for scale with AWS Lambda

We walked through a Jupyter notebook showing individual code components for processing medical claims using a single intake form. We created Python functions to extract data, validate data, gather insights, and convert those...