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	<title>PyTorch on AWS &#8211; Noise</title>
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		<title>AWS Weekly Roundup — Claude 3 Haiku in Amazon Bedrock, AWS CloudFormation optimizations, and more — March 18, 2024</title>
		<link>https://noise.getoto.net/2024/03/18/aws-weekly-roundup-claude-3-haiku-in-amazon-bedrock-aws-cloudformation-optimizations-and-more-march-18-2024/</link>
		
		<dc:creator><![CDATA[Antje Barth]]></dc:creator>
		<pubDate>Mon, 18 Mar 2024 18:22:38 +0000</pubDate>
				<category><![CDATA[Amazon Bedrock]]></category>
		<category><![CDATA[Amazon SageMaker Canvas]]></category>
		<category><![CDATA[Amazon Simple Storage Service (S3)]]></category>
		<category><![CDATA[announcements]]></category>
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		<category><![CDATA[PyTorch on AWS]]></category>
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					<description><![CDATA[Storage, storage, storage! Last week, we celebrated 18 years of innovation on Amazon Simple Storage Service (Amazon S3) at AWS Pi Day 2024. Amazon S3 mascot Buckets joined the celebrations and had a ton of fun! The 4-hour live stream was packed with puns, pie recipes powered by PartyRock, demos, code, and discussions about generative […]]]></description>
		
		
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		<title>New – Profile Your Machine Learning Training Jobs With Amazon SageMaker Debugger</title>
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		<dc:creator><![CDATA[Julien Simon]]></dc:creator>
		<pubDate>Tue, 08 Dec 2020 16:38:00 +0000</pubDate>
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					<description><![CDATA[Today, I&#8217;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 on a wide range of business problems, machine learning (ML) remains a bit of a mysterious topic. Getting things right [&#8230;]]]></description>
		
		
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		<title>New – Managed Data Parallelism in Amazon SageMaker Simplifies Training on Large Datasets</title>
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		<dc:creator><![CDATA[Julien Simon]]></dc:creator>
		<pubDate>Tue, 08 Dec 2020 16:18:17 +0000</pubDate>
				<category><![CDATA[announcements]]></category>
		<category><![CDATA[artificial intelligence]]></category>
		<category><![CDATA[AWS re:Invent]]></category>
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					<description><![CDATA[Today, I&#8217;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 grow larger and more sophisticated, machine learning (ML) practitioners working on large distributed training [&#8230;]]]></description>
		
		
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		<title>Amazon SageMaker Continues to Lead the Way in Machine Learning and Announces up to 18% Lower Prices on GPU Instances</title>
		<link>https://noise.getoto.net/2020/10/07/amazon-sagemaker-continues-to-lead-the-way-in-machine-learning-and-announces-up-to-18-lower-prices-on-gpu-instances/</link>
		
		<dc:creator><![CDATA[Julien Simon]]></dc:creator>
		<pubDate>Wed, 07 Oct 2020 16:28:52 +0000</pubDate>
				<category><![CDATA[announcements]]></category>
		<category><![CDATA[Apache MXNet on AWS]]></category>
		<category><![CDATA[artificial intelligence]]></category>
		<category><![CDATA[open source]]></category>
		<category><![CDATA[PyTorch on AWS]]></category>
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					<description><![CDATA[Since 2006, Amazon Web Services (AWS) has been helping millions of customers build and manage their IT workloads. From startups to large enterprises to public sector, organizations of all sizes use our cloud computing services to reach unprecedented levels of security, resiliency, and scalability. Every day, they&#8217;re able to experiment, innovate, and deploy to production [&#8230;]]]></description>
		
		
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