Remove Auto-classification Remove Categorization Remove Metadata
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Use custom metadata created by Amazon Comprehend to intelligently process insurance claims using Amazon Kendra

AWS Machine Learning Blog

Enterprises may want to add custom metadata like document types (W-2 forms or paystubs), various entity types such as names, organization, and address, in addition to the standard metadata like file type, date created, or size to extend the intelligent search while ingesting the documents.

Metadata 117
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LightAutoML: AutoML Solution for a Large Financial Services Ecosystem

Unite.AI

Second, the White-Box Preset implements simple interpretable algorithms such as Logistic Regression instead of WoE or Weight of Evidence encoding and discretized features to solve binary classification tasks on tabular data. The third component are the multiple machine learning pipelines stacked and/or blended to get a single prediction.

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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning Blog

However, model governance functions in an organization are centralized and to perform those functions, teams need access to metadata about model lifecycle activities across those accounts for validation, approval, auditing, and monitoring to manage risk and compliance. region_name ram_client = boto3.client('ram')

ML 87
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Time series forecasting with Amazon SageMaker AutoML

AWS Machine Learning Blog

All other columns in the dataset are optional and can be used to include additional time-series related information or metadata about each item. It provides a straightforward way to create high-quality models tailored to your specific problem type, be it classification, regression, or forecasting, among others.

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An Overview of the Top Text Annotation Tools For Natural Language Processing

John Snow Labs

Therefore, the data needs to be properly labeled/categorized for a particular use case. It allows text classification with multiple categories and offers text annotation for any script or language. Based on an auto-scaling architecture powered by Kubernetes, NLP Lab can scale to many teams and projects.