AWS Machine Learning Blog

Category: Database

Preventing customer churn by optimizing incentive programs using stochastic programming

In recent years, businesses are increasingly looking for ways to integrate the power of machine learning (ML) into business decision-making. This post demonstrates the use case of creating an optimal incentive program to offer customers identified as being at risk of leaving for a competitor, or churning. It extends a popular ML use case, predicting […]

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Accessing data sources from Amazon SageMaker R kernels

Amazon SageMaker notebooks now support R out-of-the-box, without needing you to manually install R kernels on the instances. Also, the notebooks come pre-installed with the reticulate library, which offers an R interface for the Amazon SageMaker Python SDK and enables you to invoke Python modules from within an R script. You can easily run machine […]

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Accelerating innovation: How serverless machine learning on AWS powers F1 Insights

FORMULA 1 (F1) turns 70 years old in 2020 and is one of the few sports that combines real-time skill with engineering and technical prowess. Technology has always played a central role in F1; where the evolution of the rules and tools is built into the DNA of F1. This keeps fans engaged and drivers […]

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The tech behind the Bundesliga Match Facts xGoals: How machine learning is driving data-driven insights in soccer

It’s quite common to be watching a soccer match and, when seeing a player score a goal, surmise how difficult scoring that goal was. Your opinions may be further confirmed if you’re watching the match on television and hear the broadcaster exclaim how hard it was for that shot to find the back of the […]

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Gain customer insights using Amazon Aurora machine learning

In recent years, AWS customers have been running machine learning (ML) on an increasing variety of datasets and data sources. Because a large percentage of organizational data is stored in relational databases such as Amazon Aurora, there’s a common need to make this relational data available for training ML models, and to use ML models […]

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A personalized ‘shop-by-style’ experience using PyTorch on Amazon SageMaker and Amazon Neptune

Remember the screech of the dial-up and plain-text websites? It was in that era that the Amazon.com website launched in the summer of 1995. Like the rest of the web, Amazon.com has gone through a digital experience makeover that includes slick web controls, rich media, multi-channel support, and intelligent content placement. Nonetheless, there are certain […]

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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 […]

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Anomaly detection on Amazon DynamoDB Streams using the Amazon SageMaker Random Cut Forest algorithm

Have you considered introducing anomaly detection technology to your business? Anomaly detection is a technique used to identify rare items, events, or observations which raise suspicion by differing significantly from the majority of the data you are analyzing.  The applications of anomaly detection are wide-ranging including the detection of abnormal purchases or cyber intrusions in […]

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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.

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