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Use AWS PrivateLink to set up private access to Amazon Bedrock

AWS Machine Learning

The Amazon Bedrock VPC endpoint powered by AWS PrivateLink allows you to establish a private connection between the VPC in your account and the Amazon Bedrock service account. Use the following template to create the infrastructure stack Bedrock-GenAI-Stack in your AWS account. You’re redirected to the IAM console.

APIs 124
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Contact Center Trends 2021: The CX Watershed

Fonolo

Consumers want a place to give quick feedback, vent, and interact with their favorite brands. More and more, customers simply want to solve inquires on their own – especially for simple questions like “what’s the balance on my account.” Big Data is Getting Bigger. IDC predicts that the market for Big Data will reach $16.1

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Secure Amazon SageMaker Studio presigned URLs Part 2: Private API with JWT authentication

AWS Machine Learning

To deploy the solution via the console, launch the following AWS CloudFormation template in your account by choosing Launch Stack. Alternatively, if you deployed the solution using SAM, you need to authenticate to the AWS account the solution was deployed and run sam delete. Pre-Signed URL Lambda Auth Policy. Conclusion.

APIs 81
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Use Amazon SageMaker Model Card sharing to improve model governance

AWS Machine Learning

As you scale your models, projects, and teams, as a best practice we recommend that you adopt a multi-account strategy that provides project and team isolation for ML model development and deployment. Depending on your governance requirements, Data Science & Dev accounts can be merged into a single AWS account.

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Use Amazon SageMaker Model Cards sharing to improve model governance

AWS Machine Learning

As you scale your models, projects, and teams, as a best practice we recommend that you adopt a multi-account strategy that provides project and team isolation for ML model development and deployment. Depending on your governance requirements, Data Science & Dev accounts can be merged into a single AWS account.

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

AWS Machine Learning

For provisioning Studio in your AWS account and Region, you first need to create an Amazon SageMaker domain—a construct that encapsulates your ML environment. Set up a group-level IAM role in each Studio account. If you’re looking for a scalable solution to automate your user onboarding, try this solution, and leave you feedback below!

APIs 70
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Team and user management with Amazon SageMaker and AWS SSO

AWS Machine Learning

It’s aligned with the AWS recommended practice of using temporary credentials to access AWS accounts. At the time of this writing, you can create only one domain per AWS account per Region. To implement the strong separation, you can use multiple AWS accounts with one domain per account as a workaround.