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Create a generative AI–powered custom Google Chat application using Amazon Bedrock

AWS Machine Learning

The custom Google Chat app, configured for HTTP integration, sends an HTTP request to an API Gateway endpoint. Before processing the request, a Lambda authorizer function associated with the API Gateway authenticates the incoming message. The following figure illustrates the high-level design of the solution.

APIs 119
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Onboard users to Amazon SageMaker Studio with Active Directory group-specific IAM roles

AWS Machine Learning

With SSO mode, you set up an SSO user and group in IAM Identity Center and then grant access to either the SSO group or user from the Studio console. For instance, administrators may want to set up IAM permissions for a Studio SSO user based on their Active Directory (AD) group membership.

APIs 80
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Use AWS PrivateLink to set up private access to Amazon Bedrock

AWS Machine Learning

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.

APIs 137
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Secure Amazon SageMaker Studio presigned URLs Part 3: Multi-account private API access to Studio

AWS Machine Learning

Enterprise customers have multiple lines of businesses (LOBs) and groups and teams within them. 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.

APIs 77
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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

Customers can use the SageMaker Studio UI or APIs to specify the SageMaker Model Registry model to be shared and grant access to specific AWS accounts or to everyone in the organization. The MLE is notified to set up a model group for new model development.

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Accelerate analysis and discovery of cancer biomarkers with Amazon Bedrock Agents

AWS Machine Learning

We showcase a variety of tools including database retrieval with Text2SQL, statistical models and visual charts with scientific libraries, biomedical literature search with public APIs and internal evidence, and medical image processing with Amazon SageMaker jobs. The action group contains OpenAPI schema for these actions.

APIs 53
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Improve governance of models with Amazon SageMaker unified Model Cards and Model Registry

AWS Machine Learning

Because SageMaker Model Cards and SageMaker Model Registry were built on separate APIs, it was challenging to associate the model information and gain a comprehensive view of the model development lifecycle. This model group contains all the model versions associated with that ML model. Additionally, this solution uses Amazon DataZone.