How Amazon Comprehend uses machine learning to communicate with users

As artificial intelligence and machine learning technologies grow more and more advanced, businesses have more and more tools at their disposal to analyze large amounts of data and draw valuable insights into their customers’ decision-making processes. This is particularly important when it comes to large amounts of unstructured text, from sources like customer input, social media interaction, or organizational data.

Amazon Comprehend takes on this challenge head-on. By leveraging Machine Learning (ML) algorithms with Natural Language Processing (NLP), AWS Comprehend puts the tools in your hands to analyze text-based customer interactions and make better decisions about your business strategy.

How does it work?

When you work with Ibexlabs to implement or manage your AWS data infrastructure, you will have the option to incorporate AWS Comprehend into your well-architected infrastructure.

Through ML processes, AWS Comprehend continuously adapts to new information retrieved from unstructured text. This allows, for example, the ability to scan through millions of pages of text and divide those pages according to their positive or negative sentiment.

Businesses can then set up their own specific models for AWS Comprehend to learn from, putting the powerful potential of machine learning in your hands. And as a part of an AWS well-architected infrastructure, AWS Comprehend requires no extra servers and no minimum fees. You pay only for what you use

How can I use Amazon Comprehend?

By incorporating Amazon Comprehend with Amazon Lex, customers can chat with a computerized service representative, all while Amazon Comprehend analyzes the sentiment of their messaging and responds appropriately. In conjunction with Amazon Translate, you’ll even be able to do this work across languages.

It’s not just text either. With Amazon Transcribe, your service can accurately transfer spoken word to text, analyze the software and respond appropriately, all while training your system using machine learning.

Michael Feder

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