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

Building a search solution for scanned images using Amazon Elasticsearch

In the previous chapters, we spoke about how you can use Amazon Lambda functions to create a serverless application. In this section, we will walk you through the following architecture to set up a scanned image-based search solution by calling the Amazon Textract and Amazon Comprehend APIs using an Amazon Lambda function. We are going to use Amazon Elasticsearch for this use case. However, you can also replace Amazon Elasticsearch with Amazon Kendra to create an ML-based search solution where you can use natural language to ask questions while searching.

Figure 5.2 – Building NLP search using Amazon Elasticsearch

The AWS service used in the previous architecture is Amazon Cognito to set up the login for your backend users.

Amazon S3 is used for centralized storage. Amazon Lambda functions are used as serverless event triggers when the scanned resumes are uploaded to Amazon...