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Enable fully homomorphic encryption with Amazon SageMaker endpoints for secure, real-time inferencing

AWS Machine Learning

Applications and services can call the deployed endpoint directly or through a deployed serverless Amazon API Gateway architecture. To learn more about real-time endpoint architectural best practices, refer to Creating a machine learning-powered REST API with Amazon API Gateway mapping templates and Amazon SageMaker.

Scripts 95
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How Patsnap used GPT-2 inference on Amazon SageMaker with low latency and cost

AWS Machine Learning

They use big data (such as a history of past search queries) to provide many powerful yet easy-to-use patent tools. A recent initiative is to simplify the difficulty of constructing search expressions by autofilling patent search queries using state-of-the-art text generation models. implement the model and the inference API.

APIs 67
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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 71