2012 APIs Scripts 
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. Step Functions is a serverless workflow service that can control SageMaker APIs directly through the use of the Amazon States Language.
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.
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AWS Machine Learning
OCTOBER 1, 2024
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.
AWS Machine Learning
NOVEMBER 16, 2022
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',
AWS Machine Learning
AUGUST 4, 2023
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).
AWS Machine Learning
MAY 5, 2023
Therefore, users without ML expertise can enjoy the benefits of a custom labels model through an API call, because a significant amount of overhead is reduced. A Python script is used to aid in the process of uploading the datasets and generating the manifest file. then((response) => { resolve(Buffer.from(response.data, "binary").toString("base64"));
Nexmo
FEBRUARY 14, 2020
In this tutorial, we’ll use a Nexmo Voice number to create a callback script that interacts with a caller to prompt for a voice message. Though the built-in web server should not be used in a production environment, it is fine for sample scripts like this. Make sure to replace {bucket_name} with the actual bucket name.
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