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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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Unlocking generative AI for enterprises: How SnapLogic powers their low-code Agent Creator using Amazon Bedrock

AWS Machine Learning

Agent Creator is a versatile extension to the SnapLogic platform that is compatible with modern databases, APIs, and even legacy mainframe systems, fostering seamless integration across various data environments. The integration with Amazon Bedrock is achieved through the Amazon Bedrock InvokeModel APIs.

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Deploy a Slack gateway for Amazon Bedrock

AWS Machine Learning

The Slack application sends the event to Amazon API Gateway , which is used in the event subscription. API Gateway forwards the event to an AWS Lambda function. We will cover this in a later blog post. About the Authors Rushabh Lokhande is a Senior Data & ML Engineer with AWS Professional Services Analytics Practice.

APIs 104
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Building an efficient MLOps platform with OSS tools on Amazon ECS with AWS Fargate

AWS Machine Learning

Hosted on Amazon ECS with tasks run on Fargate, this platform streamlines the end-to-end ML workflow, from data ingestion to model deployment. This blog post delves into the details of this MLOps platform, exploring how the integration of these tools facilitates a more efficient and scalable approach to managing ML projects.

APIs 120
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Generate images from text with the stable diffusion model on Amazon SageMaker JumpStart

AWS Machine Learning

We explore two ways of obtaining the same result: via JumpStart’s graphical interface on Amazon SageMaker Studio , and programmatically through JumpStart APIs. If you want to jump straight into the JumpStart API code we go through in this post, you can refer to the following sample Jupyter notebook: Introduction to JumpStart – Text to Image.

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Run text generation with GPT and Bloom models on Amazon SageMaker JumpStart

AWS Machine Learning

We explore two ways of obtaining the same result: via JumpStart’s graphical interface on Amazon SageMaker Studio , and programmatically through JumpStart APIs. Review training videos and blogs – JumpStart also provides numerous blog posts and videos that teach you how to use different functionalities within SageMaker.

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What’s All This Fuss About Composability?

ConvergeOne

The purpose of this blog post is to help folks understand why this is important and how it relates specifically to customer experience. I mentioned open APIs and microservices. This is where we can currently apply some of the remaining components such as AI, machine learning, automation, big data, and analytics.

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