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Implement smart document search index with Amazon Textract and Amazon OpenSearch

AWS Machine Learning

Documents in PDF, TIFF, JPEG or PNG format are put in an Amazon Simple Storage Service ( Amazon S3 ) bucket and subsequently indexed into OpenSearch using this Step Functions workflow. He has over 20 years of experience with internet-related technologies, engineering, and architecting solutions.

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Building Word Clouds with the Best of Them

Customer Service Life

This article was originally published on the FCR blog on April 20. Converting your word cloud to PNG format. To make the graphic look nice in PowerPoint with a transparent background, you’ll want to convert it to PNG format. Here are the steps: Step 1: Go to this website to convert the SVG file to a PNG.

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Monitoring Lake Mead drought using the new Amazon SageMaker geospatial capabilities

AWS Machine Learning

Depending on the bands, the spatial resolution of a Sentinel-2 L2A image is 10 m , 20 m , or 60 m. In some months, the surface area has reduced by more than 20% year over year. Next, we multiply that number by the area that each pixel covers to get the surface area of the water. split("_TCI")[0] mask_id = mask_file.split("/")[-1].split(".tif")[0]

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Image augmentation pipeline for Amazon Lookout for Vision

AWS Machine Learning

However, customers’ datasets usually face two problems: The number of images with anomalies could be very low and might not reach anomalies/defect type minimum imposed by Lookout for Vision (~20). The Lookout for Vision service requires at least 20 normal images and 20 anomalies per defect type. png','images/s10.png')

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Build well-architected IDP solutions with a custom lens – Part 3: Reliability

AWS Machine Learning

Accepted file formats include JPEG, PNG, PDF, and TIFF files. png','doc-img-2.png', png', 'doc-img-3.png', png', 'doc-img-4.png', png', 'doc-img-5.png'] png'] config = Config( retries = { 'max_attempts': 5, 'mode': 'adaptive' } ) client = boto3.client('textract',

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Identify rooftop solar panels from satellite imagery using Amazon Rekognition Custom Labels

AWS Machine Learning

You can create a new test dataset or split the training dataset to use 20% of the training data as the test dataset and the remaining 80% as the training dataset. When the labeling job is complete, an output.manifest file is generated and stored in the S3 output location that you specified when creating the labeling job.

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Semantic segmentation data labeling and model training using Amazon SageMaker

AWS Machine Learning

Annotations are expected to be uncompressed PNG images. png | - image2.png png |- validation_annotation | - image3.png png | - image4.png png |- label_map | - train_label_map.json | - validation_label_map.json. The annotations are single-channel PNG images. png | -0_2022-02-10T17:41:04.530266.png