SageMaker Sandbox
Project description
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
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
sagesand-0.1.1.tar.gz
(996.9 kB
view hashes)
Built Distribution
Close
Hashes for sagesand-0.1.1-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 0a76b12066b33380cd3bdd28de7894997daff1cad982dc8a41e95af6102bfafb |
|
MD5 | 433cf7ccee727b02f1dc82cde607db45 |
|
BLAKE2b-256 | 45363ad30f451499ce705324eae35f4c9f5f4bbcd5758683af2fd9bfcf18f145 |