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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.

APIs 80
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Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning

The Retrieve and RetrieveAndGenerate APIs allow your applications to directly query the index using a unified and standard syntax without having to learn separate APIs for each different vector database, reducing the need to write custom index queries against your vector store.

APIs 110
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Build and train computer vision models to detect car positions in images using Amazon SageMaker and Amazon Rekognition

AWS Machine Learning

Finally, we show how you can integrate this car pose detection solution into your existing web application using services like Amazon API Gateway and AWS Amplify. For each option, we host an AWS Lambda function behind an API Gateway that is exposed to our mock application. As always, AWS welcomes feedback. So, get started today!

APIs 63
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Secure Amazon SageMaker Studio presigned URLs Part 3: Multi-account private API access to Studio

AWS Machine Learning

In the post Secure Amazon SageMaker Studio presigned URLs Part 2: Private API with JWT authentication , we demonstrated how to build a private API to generate Amazon SageMaker Studio presigned URLs that are only accessible by an authenticated end-user within the corporate network from a single account.

APIs 69
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Reduce cost and development time with Amazon SageMaker Pipelines local mode

AWS Machine Learning

One of the main drivers for new innovations and applications in ML is the availability and amount of data along with cheaper compute options. In the data preparation step, data is loaded, massaged, and transformed into the type of inputs, or features, the ML model expects.

Scripts 71
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Improve governance of your machine learning models with Amazon SageMaker

AWS Machine Learning

Organizations can dive deep to identify which models have missing or inactive monitors and add them using SageMaker APIs to ensure all models are being checked for data drift, model drift, bias drift, and feature attribution drift. Give Model cards and the Model dashboard a try, and leave your comments with questions and feedback.