Remove categories inspect id 1
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Create high-quality datasets with Amazon SageMaker Ground Truth and FiftyOne

AWS Machine Learning

To create this app, they need a high-quality dataset containing clothing images, labeled with different categories. We add a FiftyOne classification label to each sample with the field name article_type, populated by the image’s top-level article category. A retail company is building a mobile app to help customers buy clothes.

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Automate the insurance claim lifecycle using Agents and Knowledge Bases for Amazon Bedrock

AWS Machine Learning

Knowledge base functionality is delineated through two key processes: preprocessing (Steps 1-3) and runtime (Steps 4-7): Documents undergo segmentation (chunking) into manageable sections. On the Amazon Bedrock console, navigate to the knowledge base you just created, then note the knowledge base ID under Knowledge base overview.

APIs 120
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Analyze and tag assets stored in Veeva Vault PromoMats using Amazon AppFlow and Amazon AI Services

AWS Machine Learning

This can be modified so that the flow runs on a schedule, with a maximum granularity of 1 minute. This operation detects entities in categories like Anatomy , Medical_Condition , Medication , Protected_Health_Information , and Test_Treatment_Procedure. AVAIQueuePoller – Triggered every 1 minute.

APIs 72
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Process mortgage documents with intelligent document processing using Amazon Textract and Amazon Comprehend

AWS Machine Learning

This initiates a document classification process to categorize the documents into known categories. In the next phase, we automate this process using Amazon Comprehend to classify the documents into their respective categories with high accuracy. Cell[0][1] = 16 State identification no. Document classification.

APIs 85
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Announcing the launch of the model copy feature for Amazon Rekognition Custom Labels

AWS Machine Learning

Rekognition Custom Labels builds off of the existing capabilities of Amazon Rekognition , which are already trained on tens of millions of images across many categories. region us-east-1. profile source-account { "ProjectDescriptions": [{ "ProjectArn": "arn:aws:rekognition:us-east-1::111111111111:project/rooms_1/1657588855531", }] }.

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Zero-shot text classification with Amazon SageMaker JumpStart

AWS Machine Learning

Premise Label Hypothesis A man inspects the uniform of a figure in some East Asian country. The input data includes text strings, a list of desired categories for classification, and whether the classification is multi-label or not for synchronous (real-time) inference. The following table provides some examples.

Scripts 75
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Get better insight from reviews using Amazon Comprehend

AWS Machine Learning

The model outputs generally have three components: numbered clusters (topic 0, topic 1, and so on), keywords associated to each cluster, and representative clusters for each document (or review in our case). We further explore the count of each category, and see if any duplicate data is present. Count of each categories for EDA.

APIs 67