Tag: HAQM Athena

In this post, we show you how DoorDash built a generative AI agent using HAQM Connect, HAQM Lex, and HAQM Bedrock Knowledge Bases to provide a low-latency, self-service experience for their delivery workers.

For organizations looking beyond the use of out-of-the-box Splunk AI/ML features, this post explores how HAQM SageMaker Canvas, a no-code ML development service, can be used in conjunction with data collected in Splunk to drive actionable insights. We also demonstrate how to use the generative AI capabilities of SageMaker Canvas to speed up your data exploration and help you build better ML models.

Geospatial data is data about specific locations on the earth’s surface. It can represent a geographical area as a whole or it can represent an event associated with a geographical area. Analysis of geospatial data is sought after in a few industries. It involves understanding where the data exists from a spatial perspective and why […]

The volume, velocity and variety of data has been ever increasing since the advent of the internet. The problem many enterprises face is managing this “big data” and trying to make sense out of it to yield the most desirable outcome. Siloes in enterprises, continuous ingestion of data in numerous formats, and the ever-changing technology […]

In this blog post we’ll show you how you can use HAQM Translate, HAQM Comprehend, HAQM Kinesis, HAQM Athena, and HAQM QuickSight to build a natural-language-processing (NLP)-powered social media dashboard for tweets. Social media interactions between organizations and customers deepen brand awareness. These conversations are a low-cost way to acquire leads, improve website traffic, develop […]

Have you ever had the need to segment a data set based on some of its attributes? K-means is one of the most common machine learning algorithms used to segment data. The algorithm works by separating data into different groups, called clusters. Each sample is assigned a cluster so that the samples assigned to the […]

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 HAQM Rekognition can be used to retrieve face attributes like age range, emotions, gender, etc.
How you can use HAQM Athena and HAQM 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.