This is a short refresher of the 7 AWS machine learning services announced at Re:invent 2018 which will cover:
- Amazon SageMaker Ground Truth
- Amazon Forecast
- Amazon Comprehend Medical
- Amazon Textract
- Amazon Personalize
- Amazon SageMaker RL
- AWS DeepRacer
- It aims to provide an awareness of what each of the ML services is used for and the benefit that they can bring to you within your organization
- This course would be beneficial to anyone who is responsible for implementing, managing, and securing machine learning services within AWS
- You should have a basic understanding of Machine learning concepts and principles to help you understand how each of these services fit into the AWS landscape
Related Training Content
Introduction to Machine Learning on AWS
Applying Machine Learning and AI Services on AWS
Natural Language Processing, or NLP is the process in which meaning is extracted from human language. In more general terms NLP allows systems to understand and comprehend language. Being able to do so enables the machine to interpret and understand human language. Leveraging this capability within the medical field can help and complement many things. None more so than the overall patients well-being. A quick example of this is where NLP is used to identify potential health risk problems for our patient by examining their historical clinical records.
To support this Amazon now provides a specialized version of their Amazon Comprehend service called Amazon Comprehend Medical. Amazon Comprehend Medical is a service which provides medical NLP services leveraging pretrained state of the art deep learning models. Amazon maintains the underlying machine learning models, meaning you don't need to build and train your own and this is a big positive. Using the Amazon Comprehend Medical service, you can 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, treatment and procedures, anatomy and protected health information, or PHI. When examining unstructured medical notes, you leverage the Detect Entities and Detect PHI text analysis APIs. This can be done either by 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. Here 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. Amazon Comprehend Medical has been seamlessly integrated into other existing AWS services such as Amazon S3 and AWS Lambda. This allows you to quickly roll out customized solutions that encompass and enhance clinical information gathering and processing capabilities. If you're looking for a solution that provides medical NLP analysis then Amazon Comprehend Medical is that solution.
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.
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