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Reinventing a cloud-native federated learning architecture on AWS

AWS Machine Learning Blog

Machine learning (ML), especially deep learning, requires a large amount of data for improving model performance. It is challenging to centralize such data for ML due to privacy requirements, high cost of data transfer, or operational complexity. The ML framework used at FL clients is TensorFlow.

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

Flipboard

This dataset aims to accelerate the development of event-based algorithms and methods for edge cases encountered by autonomous systems in dynamic environments. 94-171) Demonstration Noisy Measurement File from United States Census Bureau What are people doing with open data?

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Structural Evolutions in Data

O'Reilly Media

Stage 2: Machine learning models Hadoop could kind of do ML, thanks to third-party tools. But in its early form of a Hadoop-based ML library, Mahout still required data scientists to write in Java. And it (wisely) stuck to implementations of industry-standard algorithms. Those algorithms packaged with scikit-learn?

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Read graphs, diagrams, tables, and scanned pages using multimodal prompts in Amazon Bedrock

AWS Machine Learning Blog

About the Authors Mithil Shah is a Principal AI/ML Solution Architect at Amazon Web Services. He helps commercial and public sector customers use AI/ML to achieve their business outcome. Santosh Kulkarni is an Senior Solutions Architect at Amazon Web Services specializing in AI/ML.

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NLP-Powered Data Extraction for SLRs and Meta-Analyses

Towards AI

It’s also an area that stands to benefit most from automated or semi-automated machine learning (ML) and natural language processing (NLP) techniques. Over the past several years, researchers have increasingly attempted to improve the data extraction process through various ML techniques. This study by Bui et al.

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Share medical image research on Amazon SageMaker Studio Lab for free

Flipboard

Amazon SageMaker Studio Lab provides no-cost access to a machine learning (ML) development environment to everyone with an email address. Therefore, you can scale your ML experiments beyond the free compute limitations of Studio Lab and use more powerful compute instances with much bigger datasets on your AWS accounts.

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A review of purpose-built accelerators for financial services

AWS Machine Learning Blog

These activities cover disparate fields such as basic data processing, analytics, and machine learning (ML). ML is often associated with PBAs, so we start this post with an illustrative figure. The ML paradigm is learning followed by inference. The union of advances in hardware and ML has led us to the current day.

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