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

Technical requirements

For this chapter, you will need access to an AWS account. Before getting started, we recommend that you create an AWS account by referring to AWS account setup and Jupyter notebook creation steps in Technical requirements in Chapter 2, Introducing Amazon Textract. While creating an Amazon SageMaker Jupyter notebook, make sure you input AmazonComprehendFullAccess to the IAM role attached with your notebook instance, and follow these steps:

  1. Once you create the notebook instance and its status is InService, click on Open Jupyter in the Actions menu heading for the notebook instance.
  2. In the terminal window, type first cd SageMaker and then type git clone https://github.com/PacktPublishing/Natural-Language-Processing-with-AWS-AI-Services. The Python code and sample datasets for Amazon Comprehend examples are in this repository: https://github.com/PacktPublishing/Natural-Language-Processing-with-AWS-AI-Services. Once you navigate to the repository, please...