2012 Accountability APIs 
AWS Machine Learning
OCTOBER 30, 2023
It allows developers to build and scale generative AI applications using FMs through an API, without managing infrastructure. Customers are building innovative generative AI applications using Amazon Bedrock APIs using their own proprietary data.
AWS Machine Learning
JUNE 30, 2022
In this post, we will continue to build on top of the previous solution to demonstrate how to build a private API Gateway via Amazon API Gateway as a proxy interface to generate and access Amazon SageMaker presigned URLs. The user invokes createStudioPresignedUrl API on API Gateway along with a token in the header.
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AWS Machine Learning
NOVEMBER 16, 2022
When designing production CI/CD pipelines, AWS recommends leveraging multiple accounts to isolate resources, contain security threats and simplify billing-and data science pipelines are no different. Some things to note in the preceding architecture: Accounts follow a principle of least privilege to follow security best practices.
AWS Machine Learning
SEPTEMBER 23, 2024
To use a specific LLM from Amazon Bedrock, SageMaker Canvas uses the model ID of the chosen LLM as part of the API calls. Limit access to all Amazon Bedrock models To restrict access to all Amazon Bedrock models, you can modify the SageMaker role to explicitly deny these APIs. This way, users can only invoke the allowed models.
AWS Machine Learning
JULY 17, 2023
As described in the AWS Well-Architected Framework , separating workloads across accounts enables your organization to set common guardrails while isolating environments. Organizations with a multi-account architecture typically have Amazon Redshift and SageMaker Studio in two separate AWS accounts.
AWS Machine Learning
JANUARY 24, 2025
The workflow steps are as follows: The user submits an Amazon Bedrock fine-tuning job within their AWS account, using IAM for resource access. The fine-tuning job initiates a training job in the model deployment accounts. Provide your account, bucket name, and VPC settings. The following code is a sample resource policy.
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).
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