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

Extracting text from handwritten images

At this point, you are ready to start designing and building the approach. You realize that what will you build for this use case will become an extension of the existing Document Processing solution, so it will have long-term usage within the organization. So, you need to design for future scalability. With this in mind, you decide to use Amazon S3 (https://aws.amazon.com/s3/) for object storage, Amazon Textract (https://aws.amazon.com/textract/) for handwriting detection, and Amazon QuickSight (https://aws.amazon.com/quicksight/), a serverless ML-powered business intelligence service, for visualizing the insights from the handwritten content. We will be using an Amazon SageMaker Jupyter notebook for text extraction, followed by the AWS Management Console to set up the QuickSight visualizations. Let's get started.

Creating the SageMaker Jupyter notebook

If you have not done so in the previous chapters, you will have to create an...