Artificial Intelligence
Category: Storage
How Intel Olympic Technology Group built a smart coaching SaaS application by deploying pose estimation models – Part 1
February 9, 2024: Amazon Kinesis Data Firehose has been renamed to Amazon Data Firehose. Read the AWS What’s New post to learn more. The Intel Olympic Technology Group (OTG), a division within Intel focused on bringing cutting-edge technology to Olympic athletes, collaborated with AWS Machine Learning Professional Services (MLPS) to build a smart coaching software […]
Run ML inference on AWS Snowball Edge with Amazon SageMaker Edge Manager and AWS IoT Greengrass
You can use AWS Snowball Edge devices in locations like cruise ships, oil rigs, and factory floors with limited to no network connectivity for a wide range of machine learning (ML) applications such as surveillance, facial recognition, and industrial inspection. However, given the remote and disconnected nature of these devices, deploying and managing ML models […]
Simplify and automate anomaly detection in streaming data with Amazon Lookout for Metrics
Do you want to monitor your business metrics and detect anomalies in your existing streaming data pipelines? Amazon Lookout for Metrics is a service that uses machine learning (ML) to detect anomalies in your time series data. The service goes beyond simple anomaly detection. It allows developers to set up autonomous monitoring for important metrics […]
Intelligent governance of document processing pipelines for regulated industries
Processing large documents like PDFs and static images is a cornerstone of today’s highly regulated industries. From healthcare information like doctor-patient visits and bills of health, to financial documents like loan applications, tax filings, research reports, and regulatory filings, these documents are integral to how these industries conduct business. The mechanisms by which these documents […]
Using the Amazon SageMaker Studio Image Build CLI to build container images from your Studio JupyterLab notebooks
April 2025: This post was reviewed and updated for accuracy. The Amazon SageMaker Studio Image Build convenience package allows data scientists and developers to easily build custom container images from your Studio JupyterLab notebooks via CLI. The CLI eliminates the need to manually set up and connect to Docker build environments for building container images […]
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.