Analytics APIs Government Scripts 
AWS Machine Learning
JANUARY 5, 2024
MLOps – Model monitoring and ongoing governance wasn’t tightly integrated and automated with the ML models. Reusability – Without reusable MLOps frameworks, each model must be developed and governed separately, which adds to the overall effort and delays model operationalization.
AWS Machine Learning
FEBRUARY 29, 2024
The Retrieve and RetrieveAndGenerate APIs allow your applications to directly query the index using a unified and standard syntax without having to learn separate APIs for each different vector database, reducing the need to write custom index queries against your vector store.
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AWS Machine Learning
MARCH 23, 2023
Homomorphic encryption is a new approach to encryption that allows computations and analytical functions to be run on encrypted data, without first having to decrypt it, in order to preserve privacy in cases where you have a policy that states data should never be decrypted. The following figure shows both versions of these patterns.
AWS Machine Learning
FEBRUARY 13, 2024
SageMaker Feature Store now allows granular sharing of features across accounts via AWS RAM, enabling collaborative model development with governance. For example, the analytics team may curate features like customer profile, transaction history, and product catalogs in a central management account.
AWS Machine Learning
DECEMBER 21, 2022
Over the years, many table formats have emerged to support ACID transaction, governance, and catalog use cases. Apache Iceberg is an open table format for very large analytic datasets. A new optional parameter TableFormat can be set either interactively using Amazon SageMaker Studio or through code using the API or the SDK.
NobelBiz
APRIL 23, 2024
this is governed by the Fair Debt Collection Practices Act (FDCPA), which sets guidelines on how collectors can conduct themselves, the times and methods by which they can contact debtors, and the actions they are prohibited from taking. In the U.S.,
AWS Machine Learning
JULY 29, 2022
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 this post, we address the analytics and ML platform team as a consumer in the data mesh.
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