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	<title>Amazon SageMaker Ground Truth &#8211; Noise</title>
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		<title>AWS Week in Review – June 27, 2022</title>
		<link>https://noise.getoto.net/2022/06/28/aws-week-in-review-june-27-2022/</link>
		
		<dc:creator><![CDATA[Danilo Poccia]]></dc:creator>
		<pubDate>Mon, 27 Jun 2022 21:31:42 +0000</pubDate>
				<category><![CDATA[Amazon CodeWhisperer]]></category>
		<category><![CDATA[Amazon Connect]]></category>
		<category><![CDATA[Amazon DevOps Guru]]></category>
		<category><![CDATA[Amazon DynamoDB]]></category>
		<category><![CDATA[Amazon RDS]]></category>
		<category><![CDATA[Amazon RDS Custom]]></category>
		<category><![CDATA[Amazon SageMaker Ground Truth]]></category>
		<category><![CDATA[AWS Center for Quantum Computing]]></category>
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		<category><![CDATA[RDS for PostgreSQL]]></category>
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		<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[Week in Review]]></category>
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					<description><![CDATA[This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS! It’s the beginning of a new week, and I’d like to start with a recap of the most significant AWS news from the previous 7 days. Last week was special […]]]></description>
		
		
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		<title>New – Amazon SageMaker Ground Truth Now Supports Synthetic Data Generation</title>
		<link>https://noise.getoto.net/2022/06/23/new-amazon-sagemaker-ground-truth-now-supports-synthetic-data-generation/</link>
		
		<dc:creator><![CDATA[Antje Barth]]></dc:creator>
		<pubDate>Thu, 23 Jun 2022 16:34:43 +0000</pubDate>
				<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[Amazon SageMaker Ground Truth]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[artificial intelligence]]></category>
		<category><![CDATA[news]]></category>
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					<description><![CDATA[Today, I am happy to announce that you can now use Amazon SageMaker Ground Truth to generate labeled synthetic image data. Building machine learning (ML) models is an iterative process that, at a high level, starts with data collection and preparation, followed by model training and model deployment. And especially the first step, collecting large, […]]]></description>
		
		
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		<title>Serverless Architecture for a Structured Data Mining Solution</title>
		<link>https://noise.getoto.net/2021/10/01/serverless-architecture-for-a-structured-data-mining-solution/</link>
		
		<dc:creator><![CDATA[Uri Rotem]]></dc:creator>
		<pubDate>Fri, 01 Oct 2021 17:21:50 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon DynamoDB]]></category>
		<category><![CDATA[Amazon SageMaker Ground Truth]]></category>
		<category><![CDATA[Architecture]]></category>
		<category><![CDATA[AWS Lambda]]></category>
		<category><![CDATA[AWS Step Functions]]></category>
		<category><![CDATA[data cleaning]]></category>
		<category><![CDATA[data mining]]></category>
		<category><![CDATA[Manufacturing]]></category>
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					<description><![CDATA[Many businesses have an essential need for structured data stored in their own database for business operations and offerings. For example, a company that produces electronics may want to store a structured dataset of parts. This requires the following properties: color, weight, connector type, and more. This data may already be available from external sources. […]]]></description>
		
		
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		<title>Field Notes: Building an Automated Image Processing and Model Training Pipeline for Autonomous Driving</title>
		<link>https://noise.getoto.net/2021/08/03/field-notes-building-an-automated-image-processing-and-model-training-pipeline-for-autonomous-driving/</link>
		
		<dc:creator><![CDATA[Antonia Schulze]]></dc:creator>
		<pubDate>Mon, 02 Aug 2021 22:58:54 +0000</pubDate>
				<category><![CDATA[Amazon Rekognition]]></category>
		<category><![CDATA[Amazon SageMaker Ground Truth]]></category>
		<category><![CDATA[Architecture]]></category>
		<category><![CDATA[Field Notes]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[In this blog post, we demonstrate how to build an automated and scalable data pipeline for autonomous driving. This solution was built with the goal of accelerating the process of analyzing recorded footage and training a model to improve the experience of autonomous driving. We will demonstrate the extraction of images from ROS bag file […]]]></description>
		
		
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		<title>Field Notes: Automating Data Ingestion and Labeling for Autonomous Vehicle Development</title>
		<link>https://noise.getoto.net/2021/06/28/field-notes-automating-data-ingestion-and-labeling-for-autonomous-vehicle-development/</link>
		
		<dc:creator><![CDATA[Amr Ragab]]></dc:creator>
		<pubDate>Mon, 28 Jun 2021 18:35:49 +0000</pubDate>
				<category><![CDATA[Amazon Machine Learning]]></category>
		<category><![CDATA[Amazon SageMaker Ground Truth]]></category>
		<category><![CDATA[Architecture]]></category>
		<category><![CDATA[Automotive]]></category>
		<category><![CDATA[Field Notes]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[This post was co-written by Amr Ragab, AWS Sr. Solutions Architect, EC2 Engineering and Anant Nawalgaria, former AWS Professional Services EMEA. One of the most common needs we have heard from customers in Autonomous Vehicle (AV) development, is to launch a hybrid deployment environment at scale. As vehicle fleets are deployed across the globe, they […]]]></description>
		
		
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		<title>Field Notes: Gaining Insights into Labeling Jobs for Machine Learning</title>
		<link>https://noise.getoto.net/2020/09/30/field-notes-gaining-insights-into-labeling-jobs-for-machine-learning/</link>
		
		<dc:creator><![CDATA[Michael Graumann]]></dc:creator>
		<pubDate>Wed, 30 Sep 2020 14:43:57 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon QuickSight]]></category>
		<category><![CDATA[Amazon SageMaker Ground Truth]]></category>
		<category><![CDATA[Architecture]]></category>
		<category><![CDATA[Field Notes]]></category>
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					<description><![CDATA[In an era where more and more data is generated, it becomes critical for businesses to derive value from it. With the help of supervised learning, it is possible to generate models to automatically make predictions or decisions by leveraging historical data. For example, image recognition for self-driving cars, predicting anomalies on X-rays, fraud detection [&#8230;]]]></description>
		
		
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