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		<title>Iris &#8211; Turning observations into actionable insights for enhanced decision making</title>
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		<pubDate>Wed, 03 Apr 2024 01:13:10 +0000</pubDate>
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					<description><![CDATA[Introduction



Iris (/ˈaɪrɪs/), a name inspired by the Olympian mythological figure who personified the rainbow and served as the messenger of the gods, is a comprehensive observability platform for Extract, Transform, Load (ETL) jobs. Just as the myt...]]></description>
		
		
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		<title>Netflix: A Culture of Learning</title>
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		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Tue, 25 Jan 2022 16:23:45 +0000</pubDate>
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					<description><![CDATA[Martin Tingley with Wenjing Zheng, Simon Ejdemyr, Stephanie Lane, Colin McFarland, Mihir Tendulkar, and Travis BrooksThis is the last post in an overview series on experimentation at Netflix. Need to catch up? Earlier posts covered the basics of A/B te...]]></description>
		
		
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		<title>Experimentation is a major focus of Data Science across Netflix</title>
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					<description><![CDATA[Martin Tingley with Wenjing Zheng, Simon Ejdemyr, Stephanie Lane, Colin McFarland, Andy Rhines, Sophia Liu, Mihir Tendulkar, Kevin Mercurio, Veronica Hannan, Ting-Po LeeEarlier posts in this series covered the basics of A/B tests (Part 1 and Part 2 ), ...]]></description>
		
		
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		<title>Building confidence in a decision</title>
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		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Mon, 15 Nov 2021 16:20:05 +0000</pubDate>
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					<description><![CDATA[Martin Tingley with Wenjing Zheng, Simon Ejdemyr, Stephanie Lane, Michael Lindon, and Colin McFarlandThis is the fifth post in a multi-part series on how Netflix uses A/B tests to inform decisions and continuously innovate on our products. Need to catc...]]></description>
		
		
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		<title>Interpreting A/B test results: false negatives and power</title>
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		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Tue, 26 Oct 2021 15:45:24 +0000</pubDate>
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					<description><![CDATA[Martin Tingley with Wenjing Zheng, Simon Ejdemyr, Stephanie Lane, and Colin McFarlandThis is the fourth post in a multi-part series on how Netflix uses A/B tests to inform decisions and continuously innovate on our products. Need to catch up? Have a lo...]]></description>
		
		
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		<title>Interpreting A/B test results: false positives and statistical significance</title>
		<link>https://noise.getoto.net/2021/10/07/interpreting-a-b-test-results-false-positives-and-statistical-significance/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Thu, 07 Oct 2021 15:11:37 +0000</pubDate>
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					<description><![CDATA[Martin Tingley with Wenjing Zheng, Simon Ejdemyr, Stephanie Lane, and Colin McFarlandThis is the third post in a multi-part series on how Netflix uses A/B tests to inform decisions and continuously innovate on our products. Need to catch up? Have a loo...]]></description>
		
		
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		<title>What is an A/B Test?</title>
		<link>https://noise.getoto.net/2021/09/22/what-is-an-a-b-test/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Wed, 22 Sep 2021 15:54:33 +0000</pubDate>
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					<description><![CDATA[Martin Tingley with Wenjing Zheng, Simon Ejdemyr, Stephanie Lane, and Colin McFarlandThis is the second post in a multi-part series on how Netflix uses A/B tests to inform decisions and continuously innovate on our products. See here for Part 1: Decisi...]]></description>
		
		
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		<title>Decision Making at Netflix</title>
		<link>https://noise.getoto.net/2021/09/07/decision-making-at-netflix/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Tue, 07 Sep 2021 15:47:48 +0000</pubDate>
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