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

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "Copy the created bucket name, open Chapter 05/Ch05-Kendra Search.ipynb, and paste it in the following cell in place of '<your s3 bucket name>' to get started."

A block of code is set as follows:

# Define IAM role
role = get_execution_role()
print("RoleArn: {}".format(role))
sess = sagemaker.Session()
s3BucketName = '<your s3 bucket name>'
prefix = 'chapter5'

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

    <body>
        <h1>Family Bank Holdings</h1>
        <h3>Date: <span id="date"></span></h3>
        <div id="home">
          <div id="hometext">
        <h2>Who we are and what we do</h2>

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: "You will see that the page has a few headings and then a paragraph talking about Family Bank, a subsidiary of LiveRight Holdings."

Tips or Important Notes

Appear like this.