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Federated learning on AWS using FedML, Amazon EKS, and Amazon SageMaker

AWS Machine Learning

Healthcare organizations must navigate strict compliance regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States, while implementing FL solutions. FedML Octopus is the industrial-grade platform of cross-silo FL for cross-organization and cross-account training.

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Ebi.Ai Launches Free Coronabot

CSM Magazine

has applied nearly 20 years of collective experience working with big data, analytics and systems integration to discover, market and deploy a range of natural, valuable tools for all businesses across multiple sectors including automotive, insurance, property, public sector and transport & travel.

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Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD

AWS Machine Learning

Central model registry – Amazon SageMaker Model Registry is set up in a separate AWS account to track model versions generated across the dev and prod environments. Approve the model in SageMaker Model Registry in the central model registry account. Create a pull request to merge the code into the main branch of the GitHub repository.

Scripts 110
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Manufacturers Turn to Direct Sales to Bolster Customer Experience

Mindtouch

Though automotive direct sales is a legislatively fraught proposition in most parts of the country, traditional automotive manufacturers are realizing the importance of joining together the roles of producer and seller. According to Datafloq , a plug-in hybrid vehicle will generate 25 gigabytes of information in an hour of driving.

Sales 48
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Predict football punt and kickoff return yards with fat-tailed distribution using GluonTS

AWS Machine Learning

The player data was used to derive features for model development: X – Player position along the long axis of the field Y – Player position along the short axis of the field S – Speed in yards/second; replaced by Dis*10 to make it more accurate (Dis is the distance in the past 0.1

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Accelerating AI/ML development at BMW Group with Amazon SageMaker Studio

AWS Machine Learning

In contrast, the data science and analytics teams already using AWS directly for experimentation needed to also take care of building and operating their AWS infrastructure while ensuring compliance with BMW Group’s internal policies, local laws, and regulations. A data scientist team orders a new JuMa workspace in BMW’s Catalog.

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Define customized permissions in minutes with Amazon SageMaker Role Manager via the AWS CDK

AWS Machine Learning

Navigate back to your AWS CDK app home folder and run the following command to verify the generated AWS CloudFormation template: cdk synth Finally, run the following command to run the CloudFormation stack in your AWS account: cdk deploy You should see an AWS CDK deployment output similar to the one in the following screenshot.