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Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

AWS Machine Learning

Collaboration across teams – Shared features allow disparate teams like fraud, marketing, and sales to collaborate on building ML models using the same reliable data instead of creating siloed features. The second script accepts the AWS RAM invitations to discover and access cross-account feature groups from the owner level.

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Access Snowflake data using OAuth-based authentication in Amazon SageMaker Data Wrangler

AWS Machine Learning

Access and permissions to configure IDP to register Data Wrangler application and set up the authorization server or API. For data scientist: An S3 bucket that Data Wrangler can use to output transformed data. An AWS account with permissions to create AWS Identity and Access Management (IAM) policies and roles.

APIs 79
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Accelerating time-to-insight with MongoDB time series collections and Amazon SageMaker Canvas

AWS Machine Learning

Amazon SageMaker Canvas Amazon SageMaker Canvas is a visual machine learning (ML) service that enables business analysts and data scientists to build and deploy custom ML models without requiring any ML experience or having to write a single line of code. Solution overview Users persist their transactional time series data in MongoDB Atlas.

Finance 102