aws-samples/ml-lineage-helper
A wrapper around SageMaker ML Lineage Tracking extending ML Lineage to end-to-end ML lifecycles, including additional capabilities around Feature Store groups, queries, and other relevant artifacts.
When you're building machine learning models, it's crucial to understand where your training data came from and how it was processed. This tool helps you automatically record and visualize the entire history of your ML models, from raw data and feature engineering steps to the specific code versions used for training. Data scientists and ML engineers can use this to create an auditable trail of their model development.
No commits in the last 6 months.
Use this if you need to trace the origin of every piece of data, code, and feature used to create your machine learning models within AWS SageMaker.
Not ideal if your ML development workflow does not primarily use AWS SageMaker for processing, feature stores, and model training.
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Language
Python
License
Apache-2.0
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Last pushed
Oct 14, 2021
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