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Reinventing a cloud-native federated learning architecture on AWS

AWS Machine Learning Blog

Customers often need to train a model with data from different regions, organizations, or AWS accounts. Existing partner open-source FL solutions on AWS include FedML and NVIDIA FLARE. These open-source packages are deployed in the cloud by running in virtual machines, without using the cloud-native services available on AWS.

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34 new or updated datasets available on the Registry of Open Data on AWS

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The Amazon Web Services (AWS) Open Data Sponsorship Program makes high-value, cloud-optimized datasets publicly available on AWS. The full list of publicly available datasets are on the Registry of Open Data on AWS and also discoverable on the AWS Data Exchange. This quarter, AWS released 34 new or updated datasets.

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

AWS Machine Learning Blog

SnapLogic uses Amazon Bedrock to build its platform, capitalizing on the proximity to data already stored in Amazon Web Services (AWS). To address customers’ requirements about data privacy and sovereignty, SnapLogic deploys the data plane within the customer’s VPC on AWS.

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Video auto-dubbing using Amazon Translate, Amazon Bedrock, and Amazon Polly

AWS Machine Learning Blog

Faced with manual dubbing challenges and prohibitive costs, MagellanTV sought out AWS Premier Tier Partner Mission Cloud for an innovative solution. In the backend, AWS Step Functions orchestrates the preceding steps as a pipeline. Each step is run on AWS Lambda or AWS Batch. She received her Ph.D.

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Share medical image research on Amazon SageMaker Studio Lab for free

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If you have an AWS account and have set up a SageMaker Studio domain, you can also run these notebooks on Studio using the default Data Science Python kernel (with the ImJoy-jupyter-extension installed) while selecting from a variety of compute instance types. Gang Fu is a Healthcare Solution Architect at AWS.

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How SnapLogic built a text-to-pipeline application with Amazon Bedrock to translate business intent into action

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In this post, we show you how SnapLogic , an AWS customer, used Amazon Bedrock to power their SnapGPT product through automated creation of these complex DSL artifacts from human language. SnapLogic background SnapLogic is an AWS customer on a mission to bring enterprise automation to the world.

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Accelerating time-to-insight with MongoDB time series collections and Amazon SageMaker Canvas

AWS Machine Learning Blog

MongoDB’s robust time series data management allows for the storage and retrieval of large volumes of time-series data in real-time, while advanced machine learning algorithms and predictive capabilities provide accurate and dynamic forecasting models with SageMaker Canvas. Note we have two folders.