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	<title>Amazon SageMaker HyperPod &#8211; Noise</title>
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		<title>AWS Weekly Roundup: AWS Lambda Web Functions, Amazon S3 on the Vercel Marketplace, and more (October 19, 2026)</title>
		<link>https://noise.getoto.net/2026/10/19/aws-weekly-roundup-aws-lambda-web-functions-amazon-s3-on-the-vercel-marketplace-and-more-october-19-2026/</link>
		
		<dc:creator><![CDATA[Daniel Abib]]></dc:creator>
		<pubDate>Mon, 19 Oct 2026 15:34:59 +0000</pubDate>
				<category><![CDATA[Amazon Aurora]]></category>
		<category><![CDATA[Amazon Bedrock AgentCore]]></category>
		<category><![CDATA[Amazon CloudFront]]></category>
		<category><![CDATA[Amazon CloudWatch]]></category>
		<category><![CDATA[Amazon DynamoDB]]></category>
		<category><![CDATA[Amazon EC2]]></category>
		<category><![CDATA[Amazon OpenSearch Service]]></category>
		<category><![CDATA[Amazon S3 Tables]]></category>
		<category><![CDATA[Amazon SageMaker AI]]></category>
		<category><![CDATA[Amazon SageMaker HyperPod]]></category>
		<category><![CDATA[Amazon SageMaker JumpStart]]></category>
		<category><![CDATA[Amazon Simple Storage Service (S3)]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS Lambda]]></category>
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		<category><![CDATA[Week in Review]]></category>
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					<description><![CDATA[Nothing last week added a new kind of compute. What it did instead was shorten the distance between the code you already have and that code being live. AWS Lambda learned to run a plain HTTP server with no adapter in front of it, and Amazon S3 became something you can provision from inside the […]]]></description>
		
		
		
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		<title>Automate SageMaker HyperPod incident triage and root-cause-analysis with AWS DevOps Agent</title>
		<link>https://noise.getoto.net/2026/08/10/automate-sagemaker-hyperpod-incident-triage-and-root-cause-analysis-with-aws-devops-agent/</link>
		
		<dc:creator><![CDATA[Tomonori Shimomura]]></dc:creator>
		<pubDate>Mon, 10 Aug 2026 11:29:17 +0000</pubDate>
				<category><![CDATA[AI/ML]]></category>
		<category><![CDATA[AIOps]]></category>
		<category><![CDATA[Amazon SageMaker HyperPod]]></category>
		<category><![CDATA[artificial intelligence]]></category>
		<category><![CDATA[Developer Tools]]></category>
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		<category><![CDATA[Expert (400)]]></category>
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					<description><![CDATA[Introduction Large-scale machine learning workloads: training, fine-tuning, and inference run on clusters of hundreds to thousands of GPU instances for days or weeks at a stretch. Keeping operational visibility across a fleet of this size is a constant challenge: hardware health events, node lifecycle transitions, capacity fluctuations, and workload-level issues appear in the event stream around […]]]></description>
		
		
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		<title>Unlock efficient model deployment: Simplified Inference Operator setup on Amazon SageMaker HyperPod</title>
		<link>https://noise.getoto.net/2026/04/07/unlock-efficient-model-deployment-simplified-inference-operator-setup-on-amazon-sagemaker-hyperpod/</link>
		
		<dc:creator><![CDATA[Shreya Gangishetty]]></dc:creator>
		<pubDate>Mon, 06 Apr 2026 21:14:13 +0000</pubDate>
				<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[Amazon SageMaker AI]]></category>
		<category><![CDATA[Amazon SageMaker HyperPod]]></category>
		<category><![CDATA[artificial intelligence]]></category>
		<category><![CDATA[Foundational (100)]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
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					<description><![CDATA[In this post, we walk through the new installation experience, demonstrate three deployment methods (console, CLI, and Terraform), and show how features like multi-instance-type deployment and native node affinity give you fine-grained control over inference scheduling]]></description>
		
		
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		<title>Introducing checkpointless and elastic training on Amazon SageMaker HyperPod</title>
		<link>https://noise.getoto.net/2025/12/03/introducing-checkpointless-and-elastic-training-on-amazon-sagemaker-hyperpod/</link>
		
		<dc:creator><![CDATA[Channy Yun (윤석찬)]]></dc:creator>
		<pubDate>Wed, 03 Dec 2025 16:07:52 +0000</pubDate>
				<category><![CDATA[Amazon SageMaker HyperPod]]></category>
		<category><![CDATA[artificial intelligence]]></category>
		<category><![CDATA[AWS re:Invent]]></category>
		<category><![CDATA[launch]]></category>
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					<description><![CDATA[Accelerate AI model development with new training features that enable instant recovery from failures and automatic scaling based on resource availability.]]></description>
		
		
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		<title>Introducing Amazon Nova Forge: Build your own frontier models using Nova</title>
		<link>https://noise.getoto.net/2025/12/02/introducing-amazon-nova-forge-build-your-own-frontier-models-using-nova/</link>
		
		<dc:creator><![CDATA[Danilo Poccia]]></dc:creator>
		<pubDate>Tue, 02 Dec 2025 15:53:51 +0000</pubDate>
				<category><![CDATA[Amazon Bedrock]]></category>
		<category><![CDATA[Amazon Machine Learning]]></category>
		<category><![CDATA[Amazon Nova]]></category>
		<category><![CDATA[Amazon SageMaker AI]]></category>
		<category><![CDATA[Amazon SageMaker HyperPod]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[artificial intelligence]]></category>
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					<description><![CDATA[New program gives organizations unprecedented access to Nova model training, enabling them to build custom frontier models that deeply embed domain expertise without the traditional barriers of cost, compute, and time.]]></description>
		
		
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		<item>
		<title>AWS Weekly Roundup: Single GPU P5 instances, Advanced Go Driver, Amazon SageMaker HyperPod and more (August 18, 2025)</title>
		<link>https://noise.getoto.net/2025/08/18/aws-weekly-roundup-single-gpu-p5-instances-advanced-go-driver-amazon-sagemaker-hyperpod-and-more-august-18-2025/</link>
		
		<dc:creator><![CDATA[Prasad Rao]]></dc:creator>
		<pubDate>Mon, 18 Aug 2025 15:39:10 +0000</pubDate>
				<category><![CDATA[Amazon Aurora]]></category>
		<category><![CDATA[Amazon Bedrock]]></category>
		<category><![CDATA[Amazon EC2]]></category>
		<category><![CDATA[Amazon Elastic Kubernetes Service]]></category>
		<category><![CDATA[Amazon SageMaker AI]]></category>
		<category><![CDATA[Amazon SageMaker HyperPod]]></category>
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		<category><![CDATA[Week in Review]]></category>
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					<description><![CDATA[Let me start this week’s update with something I’m especially excited about – the upcoming BeSA (Become a Solutions Architect) cohort. BeSA is a free mentoring program that I host along with a few other AWS employees on a volunteer basis to help people excel in their cloud careers. Last week, the instructors’ lineup was […]]]></description>
		
		
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		<item>
		<title>Announcing Amazon Nova customization in Amazon SageMaker AI</title>
		<link>https://noise.getoto.net/2025/07/16/announcing-amazon-nova-customization-in-amazon-sagemaker-ai/</link>
		
		<dc:creator><![CDATA[Betty Zheng (郑予彬)]]></dc:creator>
		<pubDate>Wed, 16 Jul 2025 15:11:39 +0000</pubDate>
				<category><![CDATA[Amazon Nova]]></category>
		<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[Amazon SageMaker AI]]></category>
		<category><![CDATA[Amazon SageMaker HyperPod]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS Summit New York]]></category>
		<category><![CDATA[generative AI]]></category>
		<category><![CDATA[launch]]></category>
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					<description><![CDATA[AWS now enables extensive customization of Amazon Nova foundation models through SageMaker AI with techniques including continued pre-training, supervised fine-tuning, direct preference optimization, reinforcement learning from human feedback and model distillation to better address domain-specific requirements across industries.]]></description>
		
		
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		<item>
		<title>Accelerate foundation model training and fine-tuning with new Amazon SageMaker HyperPod recipes</title>
		<link>https://noise.getoto.net/2024/12/04/accelerate-foundation-model-training-and-fine-tuning-with-new-amazon-sagemaker-hyperpod-recipes/</link>
		
		<dc:creator><![CDATA[Channy Yun (윤석찬)]]></dc:creator>
		<pubDate>Wed, 04 Dec 2024 18:21:16 +0000</pubDate>
				<category><![CDATA[Amazon SageMaker HyperPod]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS re:Invent]]></category>
		<category><![CDATA[Featured]]></category>
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					<description><![CDATA[Amazon SageMaker HyperPod recipes help customers get started with training and fine-tuning popular publicly available foundation models, like Llama 3.1 405B, in just minutes with state-of-the-art performance.]]></description>
		
		
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		<title>Meet your training timelines and budgets with new Amazon SageMaker HyperPod flexible training plans</title>
		<link>https://noise.getoto.net/2024/12/04/meet-your-training-timelines-and-budgets-with-new-amazon-sagemaker-hyperpod-flexible-training-plans/</link>
		
		<dc:creator><![CDATA[Channy Yun (윤석찬)]]></dc:creator>
		<pubDate>Wed, 04 Dec 2024 16:57:34 +0000</pubDate>
				<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[Amazon SageMaker HyperPod]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[artificial intelligence]]></category>
		<category><![CDATA[AWS re:Invent]]></category>
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					<description><![CDATA[Unlock efficient large model training with SageMaker HyperPod flexible training plans - find optimal compute resources and complete training within timelines and budgets.]]></description>
		
		
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		<item>
		<title>Maximize accelerator utilization for model development with new Amazon SageMaker HyperPod task governance</title>
		<link>https://noise.getoto.net/2024/12/04/maximize-accelerator-utilization-for-model-development-with-new-amazon-sagemaker-hyperpod-task-governance/</link>
		
		<dc:creator><![CDATA[Channy Yun (윤석찬)]]></dc:creator>
		<pubDate>Wed, 04 Dec 2024 16:57:30 +0000</pubDate>
				<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[Amazon SageMaker HyperPod]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[artificial intelligence]]></category>
		<category><![CDATA[AWS re:Invent]]></category>
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					<description><![CDATA[Enable priority-based resource allocation, fair-share utilization, and automated task preemption for optimal compute utilization across teams.]]></description>
		
		
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