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AWS Machine Learning
FEBRUARY 13, 2024
For example, in an application that recommends a music playlist, features could include song ratings, listening duration, and listener demographics. SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts. Features are inputs to ML models used during training and inference.
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.
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AWS Machine Learning
JANUARY 26, 2024
Lastly, we connect these together with an example LLM workload to describe an approach towards architecting with defense-in-depth security across trust boundaries. In addition to awareness, your teams should take action to account for generative AI in governance, assurance, and compliance validation practices.
AWS Machine Learning
OCTOBER 20, 2023
However, implementing security, data privacy, and governance controls are still key challenges faced by customers when implementing ML workloads at scale. Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services.
AWS Machine Learning
JUNE 24, 2022
In the initial phase, the goal is to create a secure experimentation environment where the data scientist receives snapshots of data and experiments using SageMaker notebooks to prove that ML can solve a specific business problem. In the same account, Amazon SageMaker Feature Store can be hosted, but we don’t cover it this post.
AWS Machine Learning
MARCH 23, 2023
The predictions (inference) use encrypted data and the results are only decrypted by the end consumer (client side). To demonstrate this, we show an example of customizing an Amazon SageMaker Scikit-learn, open sourced, deep learning container to enable a deployed endpoint to accept client-side encrypted inference requests.
AWS Machine Learning
DECEMBER 21, 2022
The offline store data is stored in an Amazon Simple Storage Service (Amazon S3) bucket in your AWS account. SageMaker Feature Store automatically builds an AWS Glue Data Catalog during feature group creation. Table formats provide a way to abstract data files as a table. Iceberg has integrations with AWS services.
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