AWS Machine Learning Blog

Category: SageMaker

Data visualization and anomaly detection using Amazon Athena and Pandas from Amazon SageMaker

Many organizations use Amazon SageMaker for their machine learning (ML) requirements and source data from a data lake stored on Amazon Simple Storage Service (Amazon S3). The petabyte scale source data on Amazon S3 may not always be clean because data lakes ingest data from several source systems, such as like flat files, external feeds, […]

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Football tracking in the NFL with Amazon SageMaker

With the 2020 football season kicking off, Amazon Web Services (AWS) is continuing its work with the National Football League (NFL) on several ongoing game-changing initiatives. Specifically, the NFL and AWS are teaming up to develop state-of-the-art cloud technology using machine learning (ML) aimed at aiding the officiating process through real-time football detection. As a […]

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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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Streamline modeling with Amazon SageMaker Studio and the Amazon Experiments SDK

The modeling phase is a highly iterative process in machine learning (ML) projects, where data scientists experiment with various data preprocessing and feature engineering strategies, intertwined with different model architectures, which are then trained with disparate sets of hyperparameter values. This highly iterative process with many moving parts can, over time, manifest into a tremendous […]

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Gaining insights into winning football strategies using machine learning

University of Illinois, Urbana Champaign (UIUC) has partnered with the Amazon Machine Learning Solutions Lab to help UIUC football coaches prepare for games more efficiently and improve their odds of winning. Previously, coaches prepared for games by creating a game planning sheet that only featured types of plays for a certain down and distance, and […]

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Serving PyTorch models in production with the Amazon SageMaker native TorchServe integration

In April 2020, AWS and Facebook announced the launch of TorchServe to allow researches and machine learning (ML) developers from the PyTorch community to bring their models to production more quickly and without needing to write custom code. TorchServe is an open-source project that answers the industry question of how to go from a notebook […]

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Activity detection on a live video stream with Amazon SageMaker

Live video streams are continuously generated across industries including media and entertainment, retail, and many more. Live events like sports, music, news, and other special events are broadcast for viewers on TV and other online streaming platforms. AWS customers increasingly rely on machine learning (ML) to generate actionable insights in real time and deliver an […]

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Reducing training time with Apache MXNet and Horovod on Amazon SageMaker

Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. Amazon SageMaker removes the heavy lifting from each step of the ML process to make it easier to develop high-quality models. As datasets continue to increase in size, […]

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Using the Amazon SageMaker Studio Image Build CLI to build container images from your Studio notebooks

The new Amazon SageMaker Studio Image Build convenience package allows data scientists and developers to easily build custom container images from your Studio notebooks via a new CLI. The new CLI eliminates the need to manually set up and connect to Docker build environments for building container images in Amazon SageMaker Studio. Amazon SageMaker Studio […]

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Right-sizing resources and avoiding unnecessary costs in Amazon SageMaker

Amazon SageMaker is a fully managed service that allows you to build, train, deploy, and monitor machine learning (ML) models. Its modular design allows you to pick and choose the features that suit your use cases at different stages of the ML lifecycle. Amazon SageMaker offers capabilities that abstract the heavy lifting of infrastructure management […]

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