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GraphStorm 0.3: Scalable, multi-task learning on graphs with user-friendly APIs

AWS Machine Learning

GraphStorm is a low-code enterprise graph machine learning (GML) framework to build, train, and deploy graph ML solutions on complex enterprise-scale graphs in days instead of months. allows you to define multiple training targets on different nodes and edges within a single training loop. Specifically, GraphStorm 0.3

APIs 128
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Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

AWS Machine Learning

Features are inputs to ML models used during training and inference. Also, when features used to train models offline in batch are made available for real-time inference, it’s hard to keep the two feature stores synchronized. For a deep dive, refer to Cross account feature group discoverability and access.

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Announcing Rekogniton Custom Moderation: Enhance accuracy of pre-trained Rekognition moderation models with your data

AWS Machine Learning

In this post, we discuss how to use the Custom Moderation feature in Amazon Rekognition to enhance the accuracy of your pre-trained content moderation API. You can train a custom adapter with as few as 20 annotated images in less than 1 hour. Create a project A project is a container to store your adapters.

APIs 138
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Governing ML lifecycle at scale: Best practices to set up cost and usage visibility of ML workloads in multi-account environments

AWS Machine Learning

For a multi-account environment, you can track costs at an AWS account level to associate expenses. A combination of an AWS account and tags provides the best results. For multiple accounts, assign mandatory tags to each one, identifying its purpose and the owner responsible.

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Efficient continual pre-training LLMs for financial domains

AWS Machine Learning

Large language models (LLMs) are generally trained on large publicly available datasets that are domain agnostic. For example, Meta’s Llama models are trained on datasets such as CommonCrawl , C4 , Wikipedia, and ArXiv. The resulting LLM outperforms LLMs trained on non-domain-specific datasets when tested on finance-specific tasks.

Finance 133
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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts. Mitigation strategies : Implementing measures to minimize or eliminate risks.

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Build a multi-tenant generative AI environment for your enterprise on AWS

AWS Machine Learning

It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. API Gateway is serverless and hence automatically scales with traffic. API Gateway also provides a WebSocket API. Incoming requests to the gateway go through this point.