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

AWS Machine Learning Blog

He focuses on Deep learning including NLP and Computer Vision domains. Greg Benson is a Professor of Computer Science at the University of San Francisco and Chief Scientist at SnapLogic. Greg has published research in the areas of operating systems, parallel computing, and distributed systems.

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

Flipboard

About the Authors Greg Benson is a Professor of Computer Science at the University of San Francisco and Chief Scientist at SnapLogic. Greg has published research in the areas of operating systems, parallel computing, and distributed systems. He currently is working on Generative AI for data integration.

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Analyzing the history of Tableau innovation

Tableau

Chris had earned an undergraduate computer science degree from Simon Fraser University and had worked as a database-oriented software engineer. Incredible growth started in 2005 with the company roughly doubling in size every year until 2015. Gestalt properties including clusters are salient on scatters. Connectivity.

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Analyzing the history of Tableau innovation

Tableau

Chris had earned an undergraduate computer science degree from Simon Fraser University and had worked as a database-oriented software engineer. Incredible growth started in 2005 with the company roughly doubling in size every year until 2015. Gestalt properties including clusters are salient on scatters. Connectivity.

Tableau 98
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Financial text generation using a domain-adapted fine-tuned large language model in Amazon SageMaker JumpStart

AWS Machine Learning Blog

per diluted share, for the year ended December 31, 2015. per diluted share, for the year ended December 31, 2015. per diluted share, for the year ended December 31, 2015. per diluted share, for the year ended December 31, 2015. per diluted share, compared to $3,818,000, or $0.21

ML 97
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Federated Learning on AWS with FedML: Health analytics without sharing sensitive data – Part 2

AWS Machine Learning Blog

They were admitted to one of 335 units at 208 hospitals located throughout the US between 2014–2015. Finally, monitor and track the FL model training progression across different nodes in the cluster using the weights and biases (wandb) tool, as shown in the following screenshot. Chaoyang He is Co-founder and CTO of FedML, Inc.,

AWS 101
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Building a Predictive Model in KNIME

phData

Delving further into KNIME Analytics Platform’s Node Repository reveals a treasure trove of data science-focused nodes, from linear regression to k-means clustering to ARIMA modeling—and quite a bit in between. The great thing about building a predictive model in KNIME is its simplicity.