APIs Big data Government 
AWS Machine Learning
AUGUST 31, 2023
The framework that gives systematic visibility into ML model development, validation, and usage is called ML governance. During AWS re:Invent 2022, AWS introduced new ML governance tools for Amazon SageMaker which simplifies access control and enhances transparency over your ML projects.
AWS Machine Learning
OCTOBER 29, 2024
This post is part of an ongoing series on governing the machine learning (ML) lifecycle at scale. To start from the beginning, refer to Governing the ML lifecycle at scale, Part 1: A framework for architecting ML workloads using Amazon SageMaker. Configure centralized logging to API calls across multiple accounts using CloudTrail.
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AWS Machine Learning
DECEMBER 1, 2022
Overview of model governance. Model governance is a framework that gives systematic visibility into model development, validation, and usage. Model governance is applicable across the end-to-end ML workflow, starting from identifying the ML use case to ongoing monitoring of a deployed model through alerts, reports, and dashboards.
AWS Machine Learning
APRIL 11, 2023
These customers need to balance governance, security, and compliance against the need for machine learning (ML) teams to quickly access their data science environments in a secure manner. We also introduce a logical construct of a shared services account that plays a key role in governance, administration, and orchestration.
AWS Machine Learning
OCTOBER 2, 2024
However, scaling up generative AI and making adoption easier for different lines of businesses (LOBs) comes with challenges around making sure data privacy and security, legal, compliance, and operational complexities are governed on an organizational level. In this post, we discuss how to address these challenges holistically.
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.
AWS Machine Learning
MAY 8, 2023
It offers many native capabilities to help manage ML workflows aspects, such as experiment tracking, and model governance via the model registry. This can be a challenge for enterprises in regulated industries that need to keep strong model governance for audit purposes. Now let’s dive deeper into the details. Adds an IAM authorizer.
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