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

AWS Machine Learning

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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Use Amazon SageMaker pipeline sharing to view or manage pipelines across AWS accounts

AWS Machine Learning

On August 9, 2022, we announced the general availability of cross-account sharing of Amazon SageMaker Pipelines entities. You can now use cross-account support for Amazon SageMaker Pipelines to share pipeline entities across AWS accounts and access shared pipelines directly through Amazon SageMaker API calls.

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Provide live agent assistance for your chatbot users with Amazon Lex and Talkdesk cloud contact center

AWS Machine Learning

The Amazon Lex fulfillment AWS Lambda function retrieves the Talkdesk touchpoint ID and Talkdesk OAuth secrets from AWS Secrets Manager and initiates a request to Talkdesk Digital Connect using the Start a Conversation API. If the request to the Talkdesk API is successful, a Talkdesk conversation ID is returned to Amazon Lex.

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Optimize pet profiles for Purina’s Petfinder application using Amazon Rekognition Custom Labels and AWS Step Functions

AWS Machine Learning

The solution uses the following services: Amazon API Gateway is a fully managed service that makes it easy for developers to publish, maintain, monitor, and secure APIs at any scale. Purina’s solution is deployed as an API Gateway HTTP endpoint, which routes the requests to obtain pet attributes.

APIs 98
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Schneider Electric leverages Retrieval Augmented LLMs on SageMaker to ensure real-time updates in their ERP systems

AWS Machine Learning

Enterprise Resource Planning (ERP) systems are used by companies to manage several business functions such as accounting, sales or order management in one system. In particular, they are routinely used to store information related to customer accounts. n Question : {question}?

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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning

By the end of the consulting engagement, the team had implemented the following architecture that effectively addressed the core requirements of the customer team, including: Code Sharing – SageMaker notebooks enable data scientists to experiment and share code with other team members.

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Deploy generative AI models from Amazon SageMaker JumpStart using the AWS CDK

AWS Machine Learning

Model data is stored on Amazon Simple Storage Service (Amazon S3) in the JumpStart account. The web application interacts with the models via Amazon API Gateway and AWS Lambda functions as shown in the following diagram. Prerequisites You must have the following prerequisites: An AWS account The AWS CLI v2 Python 3.6

APIs 94