Accountability APIs Banking Government 
AWS Machine Learning
FEBRUARY 13, 2024
SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts. With this launch, account owners can grant access to select feature groups by other accounts using AWS Resource Access Manager (AWS RAM).
AWS Machine Learning
JULY 29, 2022
Create accountability on data providers from individual LoBs to share curated data assets that are discoverable, understandable, interoperable, and trustworthy. In this first post, we show the procedures of setting up a data mesh architecture with multiple AWS data producer and consumer accounts.
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AWS Machine Learning
NOVEMBER 16, 2023
With SageMaker MLOps tools, teams can easily train, test, troubleshoot, deploy, and govern ML models at scale to boost productivity of data scientists and ML engineers while maintaining model performance in production. Regulations in the healthcare industry call for especially rigorous data governance.
AWS Machine Learning
APRIL 11, 2023
These customers verify user identity by matching the user’s face in a selfie captured by a device camera with a government-issued identity card photo or preestablished profile photo. This can deter a bad actor using social media pictures of another person to open fraudulent bank accounts.
AWS Machine Learning
JUNE 24, 2022
We split the environment into multiple AWS accounts: Data lake – Stores all the ingested data from on premises (or other systems) to the cloud. The data is cataloged via the AWS Glue Data Catalog and shared with other users and accounts via AWS Lake Formation (the data governance layer).
AWS Machine Learning
JULY 29, 2022
In part 1 , we addressed the data steward persona and showcased a data mesh setup with multiple AWS data producer and consumer accounts. The data scientists in this team use Amazon SageMaker to build and train a credit risk prediction model using the shared credit risk data product from the consumer banking LoB. Data exploration.
AWS Machine Learning
SEPTEMBER 1, 2023
Each business unit has each own set of development (automated model training and building), preproduction (automatic testing), and production (model deployment and serving) accounts to productionize ML use cases, which retrieve data from a centralized or decentralized data lake or data mesh, respectively.
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