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

AWS Machine Learning

It involves training a shared ML model without moving or sharing data across sites or with a centralized server during the model training process, and can be implemented across multiple AWS accounts. Participants can either choose to maintain their data in their on-premises systems or in an AWS account that they control.

Analytics 103
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Move Amazon SageMaker Autopilot ML models from experimentation to production using Amazon SageMaker Pipelines

AWS Machine Learning

It is a sampled version of the “ Diabetes 130-US hospitals for years 1999-2008 Data Set”. Autopilot training jobs start their own dedicated SageMaker backend processes, and dedicated SageMaker API calls are required to start new training jobs, monitor training job statuses, and invoke trained Autopilot models. Prerequisites.

Scripts 101
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How Clearwater Analytics is revolutionizing investment management with generative AI and Amazon SageMaker JumpStart

AWS Machine Learning

trillion in assets across thousands of accounts worldwide. With a team of more than 1,600 professionals and a long-standing relationship with AWS dating back to 2008, Clearwater has consistently pushed the boundaries of financial technology innovation. User-identified investments can also be prohibited.

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

AWS Machine Learning

We make this possible in a few API calls in the JumpStart Industry SDK. Using the SageMaker API, we downloaded annual reports ( 10-K filings ; see How to Read a 10-K for more information) for a large number of companies. On August 21, 2009, the Company filed a Form 10-Q for the quarter ended December 31, 2008.

Finance 96
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Automated exploratory data analysis and model operationalization framework with a human in the loop

AWS Machine Learning

According to a Forbes survey , there is widespread consensus among ML practitioners that data preparation accounts for approximately 80% of the time spent in developing a viable ML model. The sample dataset we use in this post is a sampled version of the Diabetes 130-US hospitals for years 1999-2008 Data Set (Beata Strack, Jonathan P.

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A review of purpose-built accelerators for financial services

AWS Machine Learning

In terms of resulting speedups, the approximate order is programming hardware, then programming against PBA APIs, then programming in an unmanaged language such as C++, then a managed language such as Python. The CUDA API and SDK were first released by NVIDIA in 2007. GPU PBAs, 4% other PBAs, 4% FPGA, and 0.5%

Benchmark 114
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Domain-adaptation Fine-tuning of Foundation Models in Amazon SageMaker JumpStart on Financial data

AWS Machine Learning

We make this possible in a few API calls in the JumpStart Industry SDK. Using the SageMaker API, we downloaded annual reports ( 10-K filings ; see How to Read a 10-K for more information) for a large number of companies. On August 21, 2009, the Company filed a Form 10-Q for the quarter ended December 31, 2008.

Finance 52