Work through interesting real-life business use cases to uncover valuable insights from unstructured text using AWS AI services

Key Features

• Get to grips with AWS AI services for NLP and find out how to use them to gain strategic insights
• Run Python code to use Amazon Textract and Amazon Comprehend to accelerate business outcomes
• Understand how you can integrate human-in-the-loop for custom NLP use cases with Amazon A2I

Book Description

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.

What you will learn

• Automate various NLP workflows on AWS to accelerate business outcomes
• Use Amazon Textract for text, tables, and handwriting recognition from images and PDF files
• Gain insights from unstructured text in the form of sentiment analysis, topic modeling, and more using Amazon Comprehend
• Set up end-to-end document processing pipelines to understand the role of humans in the loop
• Develop NLP-based intelligent search solutions with just a few lines of code
• Create both real-time and batch document processing pipelines using Python

Who this book is for

If you're an NLP developer or data scientist looking to get started with AWS AI services to implement various NLP scenarios quickly, this book is for you. It will show you how easy it is to integrate AI in applications with just a few lines of code. A basic understanding of machine learning (ML) concepts is necessary to understand the concepts covered. Experience with Jupyter notebooks and Python will be helpful.

Table of Contents

1. NLP in the Business Context and Introduction to AWS AI Services
2. Introducing Amazon Textract
3. Introducing Amazon Comprehend
4. Automating Document Processing Workflows
5. Creating NLP Search
6. Using NLP to Improve Customer Service Efficiency
7. Understanding the Voice of Your Customer Analytics
8. Leveraging NLP to Monetize Your Media Content
9. Extracting Metadata from Financial Documents
10. Reducing Localization Costs with Machine Translation
11. Using Chatbots for Querying Documents
12. AI and NLP in Healthcare
13. Improving the Accuracy of Document Processing Workflows
14. Auditing Named Entity Recognition Workflows
15. Classifying Documents and Setting up Human in the Loop for Active Learning
16. Improving the Accuracy of PDF Batch Processing
17. Visualizing Insights from Handwritten Content
18. Building Secure, Reliable, and Efficient NLP Solutions

Review

"The book is an easy read and highly recommended for AWS developers and AI practitioners looking to add NLP capability into their business applications. The book does a great job of balancing content between explaining an AWS AI service on its own and providing an approach for building end-to-end solution workflows using that service to deliver business value.

The book covers a lot of domains (finance, healthcare, media, etc.) and AWS AI services. Each of the sections is very well structured and self-contained, which can be used as standalone reference for your specific scenarios.

I really liked the third section, which contains a lot of useful tips for improving accuracy and productionizing AI solutions beyond proof of concepts. The included notebooks and links to video snippets are succinct and help understand the concepts well."

--

Chethan Krishna, Partner Solutions Architect, Amazon Web Services

About the Authors

Mona M is a senior AI/ML specialist solutions architect at AWS. She is a highly skilled IT professional, with more than 10 years' experience in software design, development, and integration across diverse work environments. As an AWS solutions architect, her role is to ensure customer success in building applications and services on the AWS platform. She is responsible for crafting a highly scalable, flexible, and resilient cloud architecture that addresses customer business problems. She has published multiple blogs on AI and NLP on the AWS AI channel along with research papers on AI-powered search solutions.

Premkumar Rangarajan is an enterprise solutions architect, specializing in AI/ML at Amazon Web Services. He has 25 years of experience in the IT industry in a variety of roles, including delivery lead, integration specialist, and enterprise architect. He has significant architecture and management experience in delivering large-scale programs across various industries and platforms. He is passionate about helping customers solve ML and AI problems.