Accountability APIs Big data Scripts 
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
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. You can find the sample script in GitHub.
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AWS Machine Learning
AUGUST 26, 2022
We explore two ways of obtaining the same result: via JumpStart’s graphical interface on Amazon SageMaker Studio , and programmatically through JumpStart APIs. The following sections provide a step-by-step demo to perform semantic segmentation with JumpStart, both via the Studio UI and via JumpStart APIs. Solution overview.
AWS Machine Learning
MARCH 23, 2023
Applications and services can call the deployed endpoint directly or through a deployed serverless Amazon API Gateway architecture. To learn more about real-time endpoint architectural best practices, refer to Creating a machine learning-powered REST API with Amazon API Gateway mapping templates and Amazon SageMaker.
AWS Machine Learning
APRIL 11, 2023
One important aspect of this foundation is to organize their AWS environment following a multi-account strategy. In this post, we show how you can extend that architecture to multiple accounts to support multiple LOBs. In this post, we show how you can extend that architecture to multiple accounts to support multiple LOBs.
AWS Machine Learning
AUGUST 3, 2023
Finally, we show how you can integrate this car pose detection solution into your existing web application using services like Amazon API Gateway and AWS Amplify. For each option, we host an AWS Lambda function behind an API Gateway that is exposed to our mock application. iterdir(): if p_file.suffix == ".pth":
AWS Machine Learning
OCTOBER 4, 2022
Developers usually test their processing and training scripts locally, but the pipelines themselves are typically tested in the cloud. One of the main drivers for new innovations and applications in ML is the availability and amount of data along with cheaper compute options. Register model step (model package). Fail step (run failed).
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