AWS Machine Learning Blog

Category: Amazon Aurora

Generative AI-powered technology operations

Generative AI-powered technology operations

In this post we describe how AWS generative AI solutions (including Amazon Bedrock, Amazon Q Developer, and Amazon Q Business) can further enhance TechOps productivity, reduce time to resolve issues, enhance customer experience, standardize operating procedures, and augment knowledge bases.

Evolution of Cresta’s machine learning architecture: Migration to AWS and PyTorch

Cresta Intelligence, a California-based AI startup, makes businesses radically more productive by using Expertise AI to help sales and service teams unlock their full potential. Cresta is bringing together world-renowned AI thought-leaders, engineers, and investors to create a real-time coaching and management solution that transforms sales and increases service productivity, weeks after application deployment. Cresta […]

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 […]

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 […]

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.