This construct creates the foundation for developers to explore the combination of Amazon S3 Object Lambda and Amazon Comprehend for PII scenarios and it is designed with flexibility, i.e, the developers could tweak arguments via CDK to see how AWS services work and behave.

Our first Challenges of building AI-Powered APP

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Automatically generate multi-language subtitles using AWS AI/ML services. Machine generated subtitles can be edited to improve accuracy and downstream tracks will automatically be regenerated based on the edits. Built on Media Insights Engine (https://github.com/awslabs/aws-media-insights-engine)

Open innovation with 60 minute cloud experiments on AWS

⛳️ PASS: Amazon Web Services Certified (AWS Certified) Machine Learning Specialty (MLS-C01) by learning based on our Questions & Answers (Q&A) Practice Tests Exams.

Sentiment analysis using BOTO3 for Python on Amazon Comprehend

This repository contains the implementation of various AWS AI Services.

Example of integrating & using Amazon Textract, Amazon Comprehend, Amazon Comprehend Medical, Amazon Kendra to automate the processing of documents for use cases such as enterprise search and discovery, control and compliance, and general business process workflow.

As an Intelligent Process Automation showcase. We combined Business Process Management with Artificial Intelligence, and the result was awesome! Find out more: https://www.novatec-gmbh.de/en/blog/ipa-camunda-comprehend/

Dataiku DSS plugin to use the Amazon Comprehend Medical API 🩺

Dataiku DSS plugin to use the Amazon Comprehend APIs 📚

QuickSeek is a chrome extension that allows you to easily search and navigate through a YouTube video, you can quickly find and watch only parts of the video that contain words you are looking for. The Chrome extension uses Amazon Transcribe to make the audio searchable and Amazon Comprehend to perform sentiment analysis on the transcript.

A sample guide to building a serverless document processing application that can make intelligent flow-control decisions after classifying the input document type.

Live Streamed Alexa Skill development to create a searchable index of the Alexa Office Hours Archives

Final Project - Evolution of Banking vs Evolution of Blockchain Based Finance

A benchmark comparison project among the most popular sentiment analysis engines: VaderSentiment, TextBlob, Azure Text Analysis and Amazon Comprehend. The benchmarker is a python module that supports 3 datasets: IMDb, Sentiment140 and Twitter.

Analyse user sentiments and identify entities on subreddits using AWS serverless architecture.

This repository contains a series of 4 jupyter notebooks demonstrating how AWS AI Services like Amazon Rekognition, Amazon Transcribe and Amazon Comprehend can help you extract valuable metadata from your video assets and store that information in a Graph database like Amazon Neptune for maximum query performance and flexibility.

A boilerplate solution for processing image and PDF documents for regulated industries, with lineage and pipeline operations metadata services.