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Federated Learning on AWS with FedML: Health analytics without sharing sensitive data – Part 1

AWS Machine Learning Blog

In this two-part series, we demonstrate how you can deploy a cloud-based FL framework on AWS. We have developed an FL framework on AWS that enables analyzing distributed and sensitive health data in a privacy-preserving manner. In this post, we showed how you can deploy the open-source FedML framework on AWS.

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A review of purpose-built accelerators for financial services

AWS Machine Learning Blog

Examples of other PBAs now available include AWS Inferentia and AWS Trainium , Google TPU, and Graphcore IPU. This is accomplished by breaking the problem into independent parts so that each processing element can complete its part of the workload algorithm simultaneously.

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Customizing coding companions for organizations

AWS Machine Learning Blog

This retrieval can happen using different algorithms. In these two studies, commissioned by AWS, developers were asked to create a medical software application in Java that required use of their internal libraries. Xiaofei Ma is an Applied Science Manager in AWS AI Labs.

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Identifying defense coverage schemes in NFL’s Next Gen Stats

AWS Machine Learning Blog

We design an algorithm that automatically identifies the ambiguity between these two classes as the overlapping region of the clusters. This is achieved through the Guided GradCAM algorithm ( Ramprasaath et al. ). Prior to AWS, he obtained his MCS from West Virginia University and worked as computer vision researcher at Midea.

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Zero-shot prompting for the Flan-T5 foundation model in Amazon SageMaker JumpStart

AWS Machine Learning Blog

JumpStart is the machine learning (ML) hub of Amazon SageMaker that offers a one-click access to over 350 built-in algorithms; pre-trained models from TensorFlow, PyTorch, Hugging Face, and MXNet; and pre-built solution templates. He focuses on developing scalable machine learning algorithms.

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Financial text generation using a domain-adapted fine-tuned large language model in Amazon SageMaker JumpStart

AWS Machine Learning Blog

To make things easy, these three inputs depend solely on the model name, version (for a list of the available models, see Built-in Algorithms with pre-trained Model Table ), and the type of instance you want to train on. learning_rate – Controls the step size or learning rate of the optimization algorithm during training.

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Domain-adaptation Fine-tuning of Foundation Models in Amazon SageMaker JumpStart on Financial data

AWS Machine Learning Blog

To make things easy, these three inputs depend solely on the model name, version (for a list of the available models, see Built-in Algorithms with pre-trained Model Table ), and the type of instance you want to train on. learning_rate – Controls the step size or learning rate of the optimization algorithm during training.

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