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 two options to set up an intelligent search solution for your document-processing workflow. The first option involved setting up an NLP-based search quickly using Amazon Textract, Amazon Comprehend, and Amazon Elasticsearch using a Lambda function in a CloudFormation template for your scanned resume analysis, and can be used with anything scanned, such as images, invoices, or receipts. For the second option, we covered how you can set up an enterprise-level serverless scalable search solution with Amazon Kendra for your PDF documents. We also walked you through how you can enrich the Amazon Kendra search with additional attributes or metadata generated from Amazon Comprehend named entities.

In the next chapter, we will talk about how you can use AI to improve customer service in your contact center.