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	<title>Benoit de Chateauvieux &#8211; Noise</title>
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		<title>Optimize AI/ML workloads for sustainability: Part 3, deployment and monitoring</title>
		<link>https://noise.getoto.net/2022/04/27/optimize-ai-ml-workloads-for-sustainability-part-3-deployment-and-monitoring/</link>
		
		<dc:creator><![CDATA[Benoit de Chateauvieux]]></dc:creator>
		<pubDate>Wed, 27 Apr 2022 16:39:10 +0000</pubDate>
				<category><![CDATA[artificial intelligence]]></category>
		<category><![CDATA[AWS Well-Architected]]></category>
		<category><![CDATA[AWS Well-Architected Framework]]></category>
		<category><![CDATA[Optimize AI/ML workloads for sustainability]]></category>
		<category><![CDATA[Sustainability]]></category>
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					<description><![CDATA[We’re celebrating Earth Day 2022 from 4/22 through 4/29 with posts that highlight how to build, maintain, and refine your workloads for sustainability. AWS estimates that inference (the process of using a trained machine learning [ML] algorithm to make a prediction) makes up 90 percent of the cost of an ML model. Given with AWS you […]]]></description>
		
		
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		<title>Optimize AI/ML workloads for sustainability: Part 2, model development</title>
		<link>https://noise.getoto.net/2022/03/21/optimize-ai-ml-workloads-for-sustainability-part-2-model-development/</link>
		
		<dc:creator><![CDATA[Benoit de Chateauvieux]]></dc:creator>
		<pubDate>Mon, 21 Mar 2022 16:34:39 +0000</pubDate>
				<category><![CDATA[Architecture]]></category>
		<category><![CDATA[artificial intelligence]]></category>
		<category><![CDATA[AWS Well-Architected]]></category>
		<category><![CDATA[AWS Well-Architected Framework]]></category>
		<category><![CDATA[Optimize AI/ML workloads for sustainability]]></category>
		<category><![CDATA[Sustainability]]></category>
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					<description><![CDATA[More complexity often means using more energy, and machine learning (ML) models are becoming bigger and more complex. And though ML hardware is getting more efficient, the energy required to train these ML models is increasing sharply. In this series, we’re following the phases of the Well-Architected machine learning lifecycle (Figure 1) to optimize your […]]]></description>
		
		
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		<title>Optimize AI/ML workloads for sustainability: Part 1, identify business goals, validate ML use, and process data</title>
		<link>https://noise.getoto.net/2022/02/10/optimize-ai-ml-workloads-for-sustainability-part-1-identify-business-goals-validate-ml-use-and-process-data/</link>
		
		<dc:creator><![CDATA[Benoit de Chateauvieux]]></dc:creator>
		<pubDate>Thu, 10 Feb 2022 16:58:36 +0000</pubDate>
				<category><![CDATA[artificial intelligence]]></category>
		<category><![CDATA[AWS Well-Architected]]></category>
		<category><![CDATA[AWS Well-Architected Framework]]></category>
		<category><![CDATA[Optimize AI/ML workloads for sustainability]]></category>
		<category><![CDATA[Sustainability]]></category>
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					<description><![CDATA[Training artificial intelligence (AI) services and machine learning (ML) workloads uses a lot of energy—and they are becoming bigger and more complex. As an example, the Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models study estimates that a single training session for a language model like GPT-3 can have a carbon footprint […]]]></description>
		
		
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