Using natural language processing (NLP) and machine learning, Amazon Comprehend allows you to gather valuable insights from text. This course explains how!
- Learn the fundamentals of Amazon Comprehend
- Learn the three main processing models used in Comprehend
- Understand the features and benefits of the service
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 lecture, I just want to highlight some of the features and benefits of Amazon Comprehend. Firstly, I want to point out Comprehend Custom. This is a valuable feature of the service, as it allows you to take advantage of all the power of Comprehend, but with customized classifications and entities of text that are specific to your organization or business through the creation of your own machine learning NLP based models.
Using Comprehend Custom allows you to create and train new models, using data that is specific to your own use cases. This allows you to recognize specific terms that are important to your organization. For example, you might have documents containing a customer number that you wanted Comprehend to detect. Creating your own model, trained on your own data, will allow you to do this. So you might have a document with the following text. This could then be detected as follows, using your own customized model, showing the entity, the category, and the confidence rating.
API Integration. With the range of APIs offered by Amazon Comprehend, it allows a simple and flexible way to incorporate and integrate a highly sophisticated text analysis tool into your existing applications. With its straightforward implementation, you don't need to have a specialist who focuses on textual analysis in your team. Instead, you can easily take advantage of the range of capabilities offered by Comprehend, backed by machine learning and natural language processing engines and the insights that it delivers.
Service Integration. AWS have built Amazon Comprehend to be compatible with a range of other AWS services, allowing you to seamlessly collate, process, and analyze the textual results at scale. It integrates with AWS Lambda for serverless processing, Amazon S3 for storage, AWS Key Management Service for data encryption, Kinesis Data Firehose for real-time analysis, and, of course, IAM, to govern access control.
Security. I just mentioned that Comprehend integrates with IAM to manage access control and KMS for encryption, But I want to expand on this point a little further, as it's a great benefit. When work with data at scale, especially the extraction of what could be PII data, encryption should be an integral part of the solution. Amazon Comprehend allows you to encrypt any results identified using your own KMS CMK key. If you're new to AWS KMS, please see our existing course here.
Highly Scalable. A huge benefit of Amazon Comprehend is that it has the capability of analyzing millions of your documents, producing valuable insights into text being stored. If used correctly, this could be a game changer for your business, with the data that it produces. For example, enabling you to respond to customer sentiment at speed, giving you the opportunity to turn a negative into a positive.
Deep Learning. The machine learning models that underpin Amazon Comprehend's architecture are constantly being trained, using the latest data from multiple industries, domains, from across the globe. And this helps to maintain a high level of accuracy, which only gets better and better over time. Comprehend Medical.
Finally, I want to just highlight a related service of Amazon Comprehend, and that's Amazon Comprehend Medical. Thanks to NLP, which we already know is the process of extracting meaning and comprehension from language and being able to leverage this capability within the medical field, it can have many benefits, which can lead to enhancing the overall patient's wellbeing. A quick example of this is where NLP could be used to identify potential health risk problems for a patient by examining their historical clinical records. And this is where Amazon Comprehend Medical comes into play.
Comprehend Medical is a service following on from Comprehend, which will allow you to extract and identify many medical and healthcare related attributes contained within any unstructured medical text files and/or documents. Some such examples being, medications, medical conditions, treatments and procedures, anatomy, and protected health information, PHI. When examining unstructured medical notes, you leverage the detect entities and detect PHI text analysis APIs. And this can be done using the AWS CLI and/or the SDK. Relationships between extracted medical data attributes can be identified automatically, resulting in faster diagnosis, and in turn, quicker treatment decisions, recovery, and rehabilitation.
Once the analysis has been completed, jump into the AWS Management Console and open up the Comprehend Medical Console, where you can examine the relationships detected. The generated visualizations show relationships between medical entities, highlighted by connections and color encoding techniques, to differentiate the different types of medical entities.
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.