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Tag: AWS Machine Learning

AWS Localization uses Amazon Translate to scale localization

The AWS website is currently available in 16 languages (12 for the AWS Management Console and for technical documentation): Arabic, Chinese Simplified, Chinese Traditional,...

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Amazon SageMaker JumpStart solutions now support custom IAM role settings

Amazon SageMaker JumpStart solutions are a feature within Amazon SageMaker Studio that allow a simple-click experience to set up your own machine learning (ML)...

Build an air quality anomaly detector using Amazon Lookout for Metrics

Today, air pollution is a familiar environmental issue that creates severe respiratory and heart conditions, which pose serious health threats. Acid rain, depletion of...

Build a GNN-based real-time fraud detection solution using Amazon SageMaker, Amazon Neptune, and the Deep Graph Library

Fraudulent activities severely impact many industries, such as e-commerce, social media, and financial services. Frauds could cause a significant loss for businesses and consumers....

Use computer vision to measure agriculture yield with Amazon Rekognition Custom Labels

In the agriculture sector, the problem of identifying and counting the amount of fruit on trees plays an important role in crop estimation. The...

Amazon SageMaker Automatic Model Tuning now supports SageMaker Training Instance Fallbacks

Today Amazon SageMaker announced the support of SageMaker training instance fallbacks for Amazon SageMaker Automatic Model Tuning (AMT) that allow users to specify alternative...

Create Amazon SageMaker model building pipelines and deploy R models using RStudio on Amazon SageMaker

In November 2021, in collaboration with RStudio PBC, we announced the general availability of RStudio on Amazon SageMaker, the industry’s first fully managed RStudio...
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MLOps at the edge with Amazon SageMaker Edge Manager and AWS IoT Greengrass

Internet of Things (IoT) has enabled customers in multiple industries, such as manufacturing, automotive, and energy, to monitor and control real-world environments. By deploying...

Optimal pricing for maximum profit using Amazon SageMaker

This is a guest post by Viktor Enrico Jeney, Senior Machine Learning Engineer at Adspert. Adspert is...

Amazon Comprehend announces lower annotation limits for custom entity recognition

Amazon Comprehend is a natural-language processing (NLP) service you can use to automatically extract entities, key phrases, language, sentiments, and other insights from documents....

Promote feature discovery and reuse across your organization using Amazon SageMaker Feature Store and its feature-level metadata capability

Amazon SageMaker Feature Store helps data scientists and machine learning (ML) engineers securely store, discover, and share curated data used in training and prediction...

Scale YOLOv5 inference with Amazon SageMaker endpoints and AWS Lambda

After data scientists carefully come up with a satisfying machine learning (ML) model, the model must be deployed to be easily accessible for inference...

Simplify iterative machine learning model development by adding features to existing feature groups in Amazon SageMaker Feature Store

Feature engineering is one of the most challenging aspects of the machine learning (ML) lifecycle and a phase where the most amount of time...
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