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Using natural language processing (NLP) and machine learning, Amazon Comprehend allows you to gather valuable insights from text. This course explains how!

Learning Objectives

  • Learn the fundamentals of Amazon Comprehend
  • Learn the three main processing models used in Comprehend
  • Understand the features and benefits of the service

Intended Audience

This course has been designed to assist those new to Amazon Comprehend, and who are looking to learn more about how NLP and machine learning can be used to gain valuable business data to enhance your solutions.


To get the most out of this course, you should have a basic awareness of machine learning and data analytics, but it's not essential.


In this final lecture, I just want to review some of the key points made from the previous lectures. I started by explaining what Amazon Comprehend was. And in this lecture, we'll learned the following, Comprehend uses a continuously pre-trained model to identify and extract valuable insights from within the text using natural language processing. It can extract data using key phrases, sentiments, entities, personally identifiable information, language, syntax, and topic modeling. And it supports text in UTF-8 and semi-structured documents such as Word Docs or PDFs.

I then looked at some of the processing models used by Comprehend, of which there were three. Single-Document Processing, and this runs asynchronously delivering results straight back to your application requiring the data and works on a single document at a time. Multi Document Synchronous Processing, this is used when you need to run Comprehend across multiple documents at a time. And Asynchronous Batch Processing. This is basically asynchronous batch processing for analyzing texts within large documents or a large quantity of documents.

Finally, I highlighted some of the benefits and features of the service, which included Comprehend Custom, which allows you to create and train new NLP-based models, using data that is specific to your own use case in business. API integration, which offers a flexible way to incorporate and integrate a highly sophisticated text analysis tool into your existing applications. Service Integration, giving Comprehend the ability to interact with other AWS services.

Security, allowing you to encrypt your data when working with Comprehend using KMS. Highly scalable with its ability of analyzing millions of your documents, producing valuable insights into text being stored. Deep learning, providing continual training to underlying NLP models. And Comprehend Medical, which allows you to extract and identify many medical and healthcare-related attributes contained within any unstructured medical text files and/or documents.

If you'd like to get some hands-on experience with Amazon Comprehend, then please take a look at our lab, Analyzing Sentiments and Entities in text with Amazon Comprehend. That now brings me to the end of this lecture and to the end of this course. You should now have an understanding of Amazon Comprehend and how it can be used to gain insights from your text documents.

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About the Author
Stuart Scott
AWS Content Director
Learning Paths

Stuart has been working within the IT industry for two decades covering a huge range of topic areas and technologies, from data center and network infrastructure design, to cloud architecture and implementation.

To date, Stuart has created 150+ courses relating to Cloud reaching over 180,000 students, mostly within the AWS category and with a heavy focus on security and compliance.

Stuart is a member of the AWS Community Builders Program for his contributions towards AWS.

He is AWS certified and accredited in addition to being a published author covering topics across the AWS landscape.

In January 2016 Stuart was awarded ‘Expert of the Year Award 2015’ from Experts Exchange for his knowledge share within cloud services to the community.

Stuart enjoys writing about cloud technologies and you will find many of his articles within our blog pages.