SageWorks

AWS SageMaker has a fantastic set of functional components that can be used in concert to setup production level data processing and machine learning functionality.

  • Training Data: Organized S3 buckets for training data
  • Feature Store: Store/organize 'curated/known' feature sets
  • Model Registery: Models with known performance stats/Model Scoreboards
  • Model Endpoints: Easy to use HTTP(S) endpoints for single or batch predictions

Why SageWorks?

  • SageMaker is awesome but fairly complex
  • SageWorks lets us setup SageMaker Pipelines in a few lines of code
  • Pipeline Graphs: Visibility/Transparency into a Pipeline
    • What S3 data sources are getting pulled?
    • What Features Store(s) is the Model Using?
    • What's the Provenance of a Model in Model Registry?
    • What SageMaker Endpoints are associated with this model?

Installation

pip install sageworks

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.8.89)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for briford from gravatar.com briford

Credits

Author: SuperCowPowers LLC

License

Apache Software License (Apache License 2.0)

Tags

SageMaker Machine Learning AWS Python Utilities

Classifiers

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