Tag: Amazon Athena
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Principal Financial Group uses AWS Post Call Analytics solution to extract omnichannel customer insights | Amazon Web Services
An established financial services firm with over 140 years in business, Principal is a global investment management leader and serves more than 62 million...
DTCC Takes OTC Derivatives Data Access to the Cloud
DTCC has introduced a new service offering real-time
access to over-the-counter (OTC) derivatives transaction data through cloud
technology. Dubbed OTC Direct Connect, the new offering caters...
Automatically redact PII for machine learning using Amazon SageMaker Data Wrangler | Amazon Web Services
Customers increasingly want to use deep learning approaches such as large language models (LLMs) to automate the extraction of data and insights. For many...
Speed up your time series forecasting by up to 50 percent with Amazon SageMaker Canvas UI and AutoML APIs | Amazon Web Services
We’re excited to announce that Amazon SageMaker Canvas now offers a quicker and more user-friendly way to create machine learning models for time-series forecasting. SageMaker Canvas is...
Generative AI and multi-modal agents in AWS: The key to unlocking new value in financial markets | Amazon Web Services
Multi-modal data is a valuable component of the financial industry, encompassing market, economic, customer, news and social media, and risk data. Financial organizations generate,...
Visualize an Amazon Comprehend analysis with a word cloud in Amazon QuickSight | Amazon Web Services
Searching for insights in a repository of free-form text documents can be like finding a needle in a haystack. A traditional approach might be...
Amazon SageMaker Domain in VPC only mode to support SageMaker Studio with auto shutdown Lifecycle Configuration and SageMaker Canvas with Terraform | Amazon Web...
Amazon SageMaker Domain supports SageMaker machine learning (ML) environments, including SageMaker Studio and SageMaker Canvas. SageMaker Studio is a fully integrated development environment (IDE)...
Optimize equipment performance with historical data, Ray, and Amazon SageMaker | Amazon Web Services
Efficient control policies enable industrial companies to increase their profitability by maximizing productivity while reducing unscheduled downtime and energy consumption. Finding optimal control policies...
Apply fine-grained data access controls with AWS Lake Formation in Amazon SageMaker Data Wrangler | Amazon Web Services
Amazon SageMaker Data Wrangler reduces the time it takes to collect and prepare data for machine learning (ML) from weeks to minutes. You can...
Integrate SaaS platforms with Amazon SageMaker to enable ML-powered applications | Amazon Web Services
Amazon SageMaker is an end-to-end machine learning (ML) platform with wide-ranging features to ingest, transform, and measure bias in data, and train, deploy, and...
Capture public health insights more quickly with no-code machine learning using Amazon SageMaker Canvas | Amazon Web Services
Public health organizations have a wealth of data about different types of diseases, health trends, and risk factors. Their staff has long used statistical...
How Light & Wonder built a predictive maintenance solution for gaming machines on AWS | Amazon Web Services
This post is co-written with Aruna Abeyakoon and Denisse Colin from Light and Wonder (L&W). Headquartered in Las Vegas, Light & Wonder, Inc. is...
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