AWS Machine Learning Blog
Category: AWS Lambda
Turning unstructured text into insights with Bewgle powered by AWS
Bewgle is an SAP.iO, Techstars-funded company that uses AWS services to surface insights from user-generated text and audio streams. Bewgle generates insights to help product managers to increase customer satisfaction and engagement with their various products—beauty, electronics, or anything in between. By listening to the voices of their customers with the help of Bewgle powered […]
Build text analytics solutions with Amazon Comprehend and Amazon Relational Database Service
In this blog post, we will show you how to get started building rich text analytics views from your database, without having to learn anything about machine learning for natural language processing models. We’ll do this by leveraging Amazon Comprehend, paired with Amazon Aurora-MySQL and AWS Lambda.
Build automatic analysis of body language to gauge attention and engagement using Amazon Kinesis Video Streams and Amazon AI Services
August 30, 2023: Amazon Kinesis Data Analytics has been renamed to Amazon Managed Service for Apache Flink. Read the announcement in the AWS News Blog and learn more. This is a guest blog post by Ned T. Sahin, PhD (Brain Power LLC and Harvard University), Runpeng Liu (Brain Power LLC and MIT), Joseph Salisbury, PhD […]
How to Deploy Deep Learning Models with AWS Lambda and Tensorflow
Deep learning has revolutionized how we process and handle real-world data. There are many types of deep learning applications, including applications to organize a user’s photo archive, make book recommendations, detect fraudulent behavior, and perceive the world around an autonomous vehicle. In this post, we’ll show you step-by-step how to use your own custom-trained models […]
Capture and Analyze Customer Demographic Data Using Amazon Rekognition & Amazon Athena
Millions of customers shop in brick and mortar stores every day. Currently, most of these retailers have no efficient way to identify these shoppers and understand their purchasing behavior. They rely on third-party market research firms to provide customer demographic and purchase preference information.
This blog post walks you how you can use AWS services to identify purchasing behavior of your customers. We show you:
How retailers can use captured images in real time.
How Amazon Rekognition can be used to retrieve face attributes like age range, emotions, gender, etc.
How you can use Amazon Athena and Amazon QuickSight to analyze the face attributes.
How you can create unique insights and learn about customer emotions and demographics.
How to implement serverless architecture using AWS managed services.