This is the second part of a series that showcases the machine learning (ML) lifecycle with a data mesh design pattern for a large enterprise with multiple lines of business (LOBs) and a Center of Excellence (CoE) for analytics and ML.
In part 1, we addressed the data steward persona and showcased a data mesh setup with multiple AWS data producer and consumer accounts. For an overview of the business context and the steps to set up a data mesh with AWS Lake Formation and register a data product, refer to part 1.
In this post, we address the analytics and ML platform team as a consumer in the data mesh. The platform team sets up the ML environment for the data scientists and helps them get access to the necessary data products in the data mesh. The data scientists in this team use Amazon SageMaker to build and train

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