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	<title>Daren Wong &#8211; Noise</title>
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		<title>Real-time anomaly detection via Random Cut Forest in Amazon Kinesis Data Analytics</title>
		<link>https://noise.getoto.net/2023/05/01/real-time-anomaly-detection-via-random-cut-forest-in-amazon-kinesis-data-analytics/</link>
		
		<dc:creator><![CDATA[Daren Wong]]></dc:creator>
		<pubDate>Mon, 01 May 2023 18:33:40 +0000</pubDate>
				<category><![CDATA[Amazon Kinesis]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<category><![CDATA[Kinesis Data Analytics]]></category>
		<category><![CDATA[Kinesis Data Streams]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[Real-time anomaly detection describes a use case to detect and flag unexpected behavior in streaming data as it occurs. Online machine learning (ML) algorithms are popular for this use case because they don’t require any explicit rules and are able to adapt to a changing baseline, which is particularly useful for continuous streams of data […]]]></description>
		
		
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