AWS Machine Learning Blog

JANUARY 13, 2023

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. Therefore, it brings analytics to data, rather than moving data to analytics. Conclusion.

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AWS Analytics Analytics Machine Learning 104

Flipboard

DECEMBER 13, 2024

This post was written with Darrel Cherry, Dan Siddall, and Rany ElHousieny of Clearwater Analytics. To remain competitive, capital markets firms are adopting Amazon Web Services (AWS) Cloud services across the trade lifecycle to rearchitect their infrastructure, remove capacity constraints, accelerate innovation, and optimize costs.

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Analytics Analytics AWS AI 129

AWS Machine Learning Blog

JUNE 13, 2023

Overall, implementing a modern data architecture and generative AI techniques with AWS is a promising approach for gleaning and disseminating key insights from diverse, expansive data at an enterprise scale. AWS also offers foundation models through Amazon SageMaker JumpStart as Amazon SageMaker endpoints.

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Database SQL AWS AI 101

AWS Machine Learning Blog

SEPTEMBER 11, 2024

The financial services industry (FSI) is no exception to this, and is a well-established producer and consumer of data and analytics. This mostly non-technical post is written for FSI business leader personas such as the chief data officer, chief analytics officer, chief investment officer, head quant, head of research, and head of risk.

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Smart Data Collective

OCTOBER 15, 2020

Live patching is one of the most important technologies for developers working on data analytics projects on Linux. Amazon AWS reported that they developed a new live patching process that could handle large clusters of servers, which is important for working on big data applications. But how does live patching work?

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Big Data Big Data Clustering AWS 125

phData

NOVEMBER 8, 2024

These systems are built on open standards and offer immense analytical and transactional processing flexibility. However, this feature becomes an absolute must-have if you are operating your analytics on top of your data lake or lakehouse. It provided ACID transactions and built-in support for real-time analytics.

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Data Lakes Data Warehouse Database Azure 52

AWS Machine Learning Blog

FEBRUARY 10, 2023

For instance, it can reveal the preferences of play callers, allow deeper understanding of how respective coaches and teams continuously adjust their strategies based on their opponent’s strengths, and enable the development of new defensive-oriented analytics such as uniqueness of coverages ( Seth et al. ). Visualizing data using t-SNE.”

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ML ML Machine Learning Machine Learning 99