At a basic level, Machine Learning (ML) technology learns from data to make predictions. Businesses use their data with an ML-powered personalization service to elevate their customer experience. This approach allows businesses to use data to derive actionable insights and …
Machine learning (ML), especially deep learning, requires a large amount of data for improving model performance. Customers often need to train a model with data from different regions, organizations, or AWS accounts. It is challenging to centralize such data for …
We’re excited to announce Amazon SageMaker Data Wrangler support for Amazon S3 Access Points. With its visual point and clikc interface, SageMaker Data Wrangler simplifies the process of data preparation and feature engineering including data selection, cleansing, exploration, and visualization, …
Amazon Kendra is an intelligent search service powered by machine learning (ML). Amazon Kendra reimagines enterprise search for your websites and applications so your employees and customers can easily find the content they’re looking for, even when it’s scattered across …
Healthcare data is complex and siloed, and exists in various formats. An estimated 80% of data within organizations is considered to be unstructured or “dark” data that is locked inside text, emails, PDFs, and scanned documents. This data is difficult …
This is a guest post co-written with Fred Wu from Sportradar.
Sportradar is the world’s leading sports technology company, at the intersection between sports, media, and betting. More than 1,700 sports federations, media outlets, betting operators, and consumer platforms across …
This post was co-written with Dave Gowel, CEO of RallyPoint. In his own words, “RallyPoint is an online social and professional network for veterans, service members, family members, caregivers, and other civilian supporters of the US armed forces. With …