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New – Store, Discover, and Share Machine Learning Features with Amazon SageMaker Feature Store

Today, I’m extremely happy to announce Amazon SageMaker Feature Store, a new capability of Amazon SageMaker that makes it easy for data scientists and machine learning engineers to securely store, discover and share curated data used…
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Amazon HealthLake Stores, Transforms, and Analyzes Health Data in the Cloud

Healthcare organizations collect vast amounts of patient information every day, from family history and clinical observations to diagnoses and medications. They use all this data to try to compile a complete picture of a patient’s health…
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Preview: Amazon Lookout for Metrics, an Anomaly Detection Service for Monitoring the Health of Your Business

We are excited to announce Amazon Lookout for Metrics, a new service that uses machine learning (ML) to detect anomalies in your metrics, helping you proactively monitor the health of your business, diagnose issues, and find opportunities…
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Amazon SageMaker Edge Manager Simplifies Operating Machine Learning Models on Edge Devices

Today, I’m extremely happy to announce Amazon SageMaker Edge Manager, a new capability of Amazon SageMaker that makes it easier to optimize, secure, monitor, and maintain machine learning models on a fleet of edge devices.Edge computing…
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New – Amazon SageMaker Clarify Detects Bias and Increases the Transparency of Machine Learning Models

Today, I’m extremely happy to announce Amazon SageMaker Clarify, a new capability of Amazon SageMaker that helps customers detect bias in machine learning (ML) models, and increase transparency by helping explain model behavior to stakeholders…
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New – Profile Your Machine Learning Training Jobs With Amazon SageMaker Debugger

Today, I’m extremely happy to announce that Amazon SageMaker Debugger can now profile machine learning models, making it much easier to identify and fix training issues caused by hardware resource usage.Despite its impressive performance…
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New – Managed Data Parallelism in Amazon SageMaker Simplifies Training on Large Datasets

Today, I’m particularly happy to announce that Amazon SageMaker now supports a new data parallelism library that makes it easier to train models on datasets that may be as large as hundreds or thousands of gigabytes. As data sets and models…
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Amazon SageMaker Simplifies Training Deep Learning Models With Billions of Parameters

Today, I’m extremely happy to announce that Amazon SageMaker simplifies the training of very large deep learning models that were previously difficult to train due to hardware limitations. In the last 10 years, a subset of machine learning…
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In the Works – AWS Region in Melbourne, Australia

We launched new AWS Regions in Italy and South Africa in 2020, and are working on regions in Indonesia, Japan, Spain, India, and Switzerland.Melbourne, Australia in 2020Today I am happy to announce that the Asia Pacific (Melbourne) region…
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re:Invent 2020 Liveblog: Machine Learning Keynote

Follow along as AWS Chief Evangelist Jeff Barr and Developer Advocates Martin Beeby and Steve Roberts liveblog the first-ever Machine Learning Keynote. Swami Sivasubramanian will share the latest developments and launches in AWS machine…