AWS Machine Learning Blog

Tag: Amazon Machine Learning

Use Amazon SageMaker Data Wrangler in Amazon SageMaker Studio with a default lifecycle configuration

If you use the default lifecycle configuration for your domain or user profile in Amazon SageMaker Studio and use Amazon SageMaker Data Wrangler for data preparation, then this post is for you. In this post, we show how you can create a Data Wrangler flow and use it for data preparation in a Studio environment […]

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Predict types of machine failures with no-code machine learning using Amazon SageMaker Canvas

Predicting common machine failure types is critical in manufacturing industries. Given a set of characteristics of a product that is tied to a given type of failure, you can develop a model that can predict the failure type when you feed those attributes to a machine learning (ML) model. ML can help with insights, but […]

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Your guide to artificial Intelligence and machine learning at re:Invent 2019

With less than 40 days to re:Invent 2019, the excitement is building up and we are looking forward to seeing you all soon! Continuing our journey on artificial intelligence and machine learning, we are bringing a lot of technical content this year, with over 200 breakout sessions, deep-dive chalk talks, hands-on exercises with workshops featuring […]

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AWS supports the Deepfake Detection Challenge with competition data and AWS credits

Today AWS is pleased to announce that it is working with Facebook, Microsoft, and the Partnership on AI on the first Deepfakes Detection Challenge.  The competition, to which we are contributing up to $1 million in AWS credits to researchers and academics over the next two years, is designed to produce technology that can be […]

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Predicting Customer Churn with Amazon Machine Learning

Note: This post has a companion talk that was delivered at AWS re:Invent 2016. Losing customers is costly for any business. Identifying unhappy customers early on gives you a chance to offer them incentives to stay. This post describes using machine learning (ML) for the automated identification of unhappy customers, also known as customer churn […]

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