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

Summary

After having learned how to build NLP solutions for a number of real-world use cases in the previous chapters, we spent this chapter reading about how to build secure, reliable, and efficient architectures using the AWS Well-Architected Framework. We first introduced what the Well-Architected Framework is, and reviewed the five pillars it is comprised of: operational excellence, security, reliability, performance efficiency, and cost optimization. We read about each of the pillars in brief, and then discussed how the Well-Architected Framework can help us build better and more efficient NLP solutions by using a matrix of best practices aligned with the Well-Architected principles and the different stages of NLP solution development.

We followed this summary of the best practices by diving deep into each one, learning how to implement them using the AWS Management Console, AWS documentation references, and some code snippets.

That brings us to the end of this book. It...