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In this post, we look at how we can use AWS Glue and the AWS Lake Formation ML transform FindMatches to harmonize (deduplicate) customer data coming from different sources to get a complete customer profile to be able to provide better customer experience. Run the AWS Glue ML transform job.
Traditionally, answering this question would involve multiple data exports, complex extract, transform, and load (ETL) processes, and careful data synchronization across systems. SageMaker Unified Studio provides a unified experience for using data, analytics, and AI capabilities.
If prompted, set up a user profile for SageMaker Studio by providing a user name and specifying AWS Identity and Access Management (IAM) permissions. AWS SDKs and authentication Verify that your AWS credentials (usually from the SageMaker role) have Amazon Bedrock access. Open a SageMaker Studio notebook: Choose JupyterLab.
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