SageMaker Sandbox
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 SageMaker Sandbox?
- SageMaker is awesome but fairly complex
- Spider 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 sagesand
1 maintainer
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