<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:dc="http://purl.org/dc/elements/1.1/">
  <channel>
    <title>DEV Community: Senthamizhselvan Krishnamoorthy</title>
    <description>The latest articles on DEV Community by Senthamizhselvan Krishnamoorthy (@sandykrishna).</description>
    <link>https://dev.to/sandykrishna</link>
    <image>
      <url>https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F1133547%2Fadca8698-9a8c-4e8e-a723-2ba5ade52a99.jpeg</url>
      <title>DEV Community: Senthamizhselvan Krishnamoorthy</title>
      <link>https://dev.to/sandykrishna</link>
    </image>
    <atom:link rel="self" type="application/rss+xml" href="https://dev.to/feed/sandykrishna"/>
    <language>en</language>
    <item>
      <title>Automation of Sagemaker using Lambda</title>
      <dc:creator>Senthamizhselvan Krishnamoorthy</dc:creator>
      <pubDate>Tue, 12 Mar 2024 04:24:59 +0000</pubDate>
      <link>https://dev.to/sandykrishna/automation-of-sagemaker-using-lambda-o01</link>
      <guid>https://dev.to/sandykrishna/automation-of-sagemaker-using-lambda-o01</guid>
      <description>&lt;p&gt;Amazon SageMaker is a fully managed machine learning service. They provide a number of tools to label, build, train, deploy and monitor machine learning models in a production-ready hosted environment. It provides an integrated Jupyter authoring notebook instance for easy access to your data sources for exploration and analysis, so you don’t have to manage servers.&lt;/p&gt;

</description>
    </item>
  </channel>
</rss>
