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

Setting up an enterprise search solution using Amazon Kendra

In this section, we will cover how you can quickly create an end-to-end serverless document search application using Amazon Kendra.

In this section, we will cover the steps to get started.

Git cloning the notebook

We will walk through the steps to git clone the notebook and show code samples to set up the kendra based search architecture using simple boto3 APIs.

  1. In the SageMaker Jupyter notebook you set up in the previous chapters, Git clone https://github.com/PacktPublishing/Natural-Language-Processing-with-AWS-AI-Services/.
  2. Go to Chapter 05/Ch05-Kendra Search.ipynb and start running the notebook.

    Note:

    Please add Kendra IAM access to the SageMaker notebook IAM role so that you can call Kendra APIs through this notebook. In previous chapters, you already added IAM access to Amazon Comprehend and Textract APIs from the SageMaker notebook.

Creating an Amazon S3 bucket

We will show you how you can...