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Bring legacy machine learning code into Amazon SageMaker using AWS Step Functions

AWS Machine Learning

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. Step Functions is a serverless workflow service that can control SageMaker APIs directly through the use of the Amazon States Language.

Scripts 144
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Securing MLflow in AWS: Fine-grained access control with AWS native services

AWS Machine Learning

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.

APIs 97
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Build a serverless voice-based contextual chatbot for people with disabilities using Amazon Bedrock

AWS Machine Learning

You only consume the services through their API. To understand better how Amazon Cognito allows external applications to invoke AWS services, refer to refer to Secure API Access with Amazon Cognito Federated Identities, Amazon Cognito User Pools, and Amazon API Gateway. We discuss this later in the post.

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Build a cross-account MLOps workflow using the Amazon SageMaker model registry

AWS Machine Learning

The following code illustrates this policy, but don’t add it to the shared services account yet: #Data Science account's policy to access Shared Services' S3 bucket { 'Version': '2012-10-17', 'Statement': [{ 'Sid': 'AddPerm', 'Effect': 'Allow', 'Principal': { 'AWS': 'arn:aws:iam:: :root' }, "Action": [. 's3:PutObject',

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Use the Amazon SageMaker and Salesforce Data Cloud integration to power your Salesforce apps with AI/ML

AWS Machine Learning

When a version of the model in the Amazon SageMaker Model Registry is approved, the endpoint is exposed as an API with Amazon API Gateway using a custom Salesforce JSON Web Token (JWT) authorizer. frameworks to restrict client access to your APIs. For API Name , leave as default (it’s automatically populated).

APIs 98
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Separate lines of business or teams with multiple Amazon SageMaker domains

AWS Machine Learning

However, if you want to update existing resources to facilitate resource isolation, administrations can use the add-tag SageMaker API call in a script. Since the launch of the multi-domain capability, new resources are automatically tagged with aws:ResourceTag/sagemaker:domain-arn. experiments=`aws --region $REGION.

APIs 95
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Four approaches to manage Python packages in Amazon SageMaker Studio notebooks

AWS Machine Learning

When you open a notebook in Studio, you are prompted to set up your environment by choosing a SageMaker image, a kernel, an instance type, and, optionally, a lifecycle configuration script that runs on image startup. The main benefit is that a data scientist can choose which script to run to customize the container with new packages.