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	<title>Ayush Kulkarni &#8211; Noise</title>
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		<title>Deploying AI models for inference with AWS Lambda using zip packaging</title>
		<link>https://noise.getoto.net/2025/10/03/deploying-ai-models-for-inference-with-aws-lambda-using-zip-packaging/</link>
		
		<dc:creator><![CDATA[Ayush Kulkarni]]></dc:creator>
		<pubDate>Thu, 02 Oct 2025 22:11:33 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[AWS Lambda]]></category>
		<category><![CDATA[serverless]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[Users usually package their function code as container images when using machine learning (ML) models that are larger than 250 MB, which is the Lambda deployment package size limit for zip files. In this post, we demonstrate an approach that downloads ML models directly from Amazon S3 into your function’s memory so that you can continue packaging your function code using zip files.]]></description>
		
		
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		<title>Under the hood: how AWS Lambda SnapStart optimizes function startup latency</title>
		<link>https://noise.getoto.net/2025/08/19/under-the-hood-how-aws-lambda-snapstart-optimizes-function-startup-latency/</link>
		
		<dc:creator><![CDATA[Ayush Kulkarni]]></dc:creator>
		<pubDate>Tue, 19 Aug 2025 20:01:36 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[AWS Lambda]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[AWS Lambda cold start latency can impact performance for latency-sensitive applications, with function initialization being the primary contributor to startup delays. Lambda SnapStart addresses this challenge by reducing cold start times from several seconds to sub-second performance for Java, Python, and .NET runtimes with minimal code changes. This post explains SnapStart's underlying mechanisms and provides performance optimization recommendations for applications using this feature.]]></description>
		
		
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