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Preprocess and fine-tune LLMs quickly and cost-effectively using Amazon EMR Serverless and Amazon SageMaker

AWS Big Data

The Common Crawl corpus contains petabytes of data, regularly collected since 2008, and contains raw webpage data, metadata extracts, and text extracts. In addition to determining which dataset should be used, cleansing and processing the data to the fine-tuning’s specific need is required.

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Use your corporate identities for analytics with Amazon EMR and AWS IAM Identity Center

AWS Big Data

int '2' 'InstanceType': 'Ref': 'ClusterInstanceType' 'Market': 'ON_DEMAND' 'Name': 'Core' 'Outputs': 'ClusterId': 'Value': 'Ref': 'EmrCluster' 'Description': 'The ID of the EMR cluster' 'Metadata': 'AWS::CloudFormation::Designer': {} 'Rules': {} Trusted identity propagation is supported from Amazon EMR 6.15

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Themes and Conferences per Pacoid, Episode 12

Domino Data Lab

I mention this here because there was a lot of overlap between current industry data governance needs and what the scientific community is working toward for scholarly infrastructure. The gist is, leveraging metadata about research datasets, projects, publications, etc., 2008 – Financial crisis : scientists flee Wall St.