2012 APIs Best practices Scripts 
AWS Machine Learning
NOVEMBER 16, 2022
For an example account structure to follow organizational unit best practices to host models using SageMaker endpoints across accounts, refer to MLOps Workload Orchestrator. Some things to note in the preceding architecture: Accounts follow a principle of least privilege to follow security best practices. Prerequisites.
AWS Machine Learning
MARCH 15, 2023
The best practice for migration is to refactor these legacy codes using the Amazon SageMaker API or the SageMaker Python SDK. SageMaker runs the legacy script inside a processing container. SageMaker takes your script, copies your data from Amazon Simple Storage Service (Amazon S3), and then pulls a processing container.
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AWS Machine Learning
MAY 8, 2023
In this post, we address these limitations by implementing the access control outside of the MLflow server and offloading authentication and authorization tasks to Amazon API Gateway , where we implement fine-grained access control mechanisms at the resource level using Identity and Access Management (IAM). Adds an IAM authorizer.
AWS Machine Learning
DECEMBER 14, 2023
After you stop the Space, you can modify its settings using either the UI or API via the updated SageMaker Studio interface and then restart the Space. This setup enables you to centrally store notebooks, scripts, and other project files, accessible across all your SageMaker Studio sessions and instances.
AWS Machine Learning
SEPTEMBER 7, 2023
In this post, we provide some best practices to maximize the value of SageMaker Pipelines and make the development experience seamless. Best practices for SageMaker Pipelines In this section, we discuss some best practices that can be followed while designing workflows using SageMaker Pipelines.
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