https://github.com/shivasairam1706/mlops-project1

End-to-end ML-Ops project using PySpark and AWS, covering environment setup, model training, deployment with data capture, execution, and analysis. CI/CD pipelines (AWS CodePipeline) and monitoring (CloudWatch) ensure automated deployment, performance tracking, and model retraining for production-ready ML solutions.
https://github.com/shivasairam1706/mlops-project1

aws aws-lambda aws-s3 data-engineering data-science data-visualization delta-lake docker forcasting mlops-project pyspark unix-shell

Last synced: 14 days ago
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End-to-end ML-Ops project using PySpark and AWS, covering environment setup, model training, deployment with data capture, execution, and analysis. CI/CD pipelines (AWS CodePipeline) and monitoring (CloudWatch) ensure automated deployment, performance tracking, and model retraining for production-ready ML solutions.

• Host: GitHub
• URL: https://github.com/shivasairam1706/mlops-project1
• Owner: Shivasairam1706
• Created: 2025-02-16T08:48:19.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-02-26T20:02:55.000Z (over 1 year ago)
• Last Synced: 2025-02-26T20:35:42.297Z (over 1 year ago)
• Topics: aws, aws-lambda, aws-s3, data-engineering, data-science, data-visualization, delta-lake, docker, forcasting, mlops-project, pyspark, unix-shell
• Homepage:
• Size: 28.3 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
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