AWS Machine Learning Blog

Build XGBoost models with Amazon Redshift ML

Amazon Redshift ML allows data analysts, developers, and data scientists to train machine learning (ML) models using SQL. In previous posts, we demonstrated how customers can use the automatic model training capability of Amazon Redshift to train their classification and regression models. Redshift ML provides several capabilities for data scientists. It allows you to create […]

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Automate Amazon SageMaker Studio setup using AWS CDK

Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). Studio provides a single web-based visual interface where you can perform all ML development steps required to prepare data, as well as build, train, and deploy models. You can quickly upload data, create new notebooks, train and tune models, move […]

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Connect to your Amazon CloudWatch data to detect anomalies and diagnose their root cause using Amazon Lookout for Metrics

Amazon Lookout for Metrics uses machine learning (ML) to automatically detect and diagnose anomalies (outliers from the norm) without requiring any prior ML experience. Amazon CloudWatch provides you with actionable insights to monitor your applications, respond to system-wide performance changes, optimize resource utilization, and get a unified view of operational health. This post demonstrates how […]

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Event-based fraud detection with direct customer calls using Amazon Connect

Several recent surveys show that more than 80% of consumers prefer spending with a credit card over cash. Thanks to advances in AI and machine learning (ML), credit card fraud can be detected quickly, which makes credit cards one of the safest and easiest payment methods to use. The challenge with cards, however, is that […]

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Build patient outcome prediction applications using Amazon HealthLake and Amazon SageMaker

Healthcare data can be challenging to work with and AWS customers have been looking for solutions to solve certain business challenges with the help of data and machine learning (ML) techniques. Some of the data is structured, such as birthday, gender, and marital status, but most of the data is unstructured, such as diagnosis codes […]

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Build multi-class classification models with Amazon Redshift ML

Amazon Redshift ML simplifies the use of machine learning (ML) by using simple SQL statements to create and train ML models from data in Amazon Redshift. You can use Amazon Redshift ML to solve binary classification, multi-class classification, and regression problems and can use either AutoML or XGBoost directly. This post is part of a […]

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How to run an AI powered musical challenge: “AWS DeepComposer Got Talent”

To help you fast track your company’s adoption of machine learning (ML), AWS offers educational solutions for developers to get hands-on experience. We like to think of these programs as a fun way for developers to build their skills using ML technologies in real world scenarios. In this post, we walk you through how to […]

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Develop and deploy ML models using Amazon SageMaker Data Wrangler and Amazon SageMaker Autopilot

Data generates new value to businesses through insights and building predictive models. However, although data is plentiful, available data scientists are far and few. Despite our attempts in recent years to produce data scientists from academia and elsewhere, we still see a huge shortage that will continue into the near future. To accelerate model building, […]

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Save costs by automatically shutting down idle resources within Amazon SageMaker Studio

Amazon SageMaker Studio provides a unified, web-based visual interface where you can perform all machine learning (ML) development steps, making data science teams up to 10 times more productive. Studio gives you complete access, control, and visibility into each step required to build, train, and deploy models. Studio notebooks are collaborative notebooks that you can […]

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Deliver personalized customer support experiences with Amazon Connect, Amazon Lex, and Salesforce

The last year has made delivering high-quality customer contact center support extremely challenging. Consumers have increasingly abandoned brick-and-mortar retail shopping and traditional banking in favor of digitally enabled experiences, which brings unprecedented call volumes to contact centers. In many cases, call center staff are also working remotely, which makes it even more difficult to meet […]

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