aws-samples / documentdb-sagemaker-example
We use Amazon SageMaker to analyze data stored in Amazon DocumentDB. After showing how to write queries to conduct a descriptive analysis, we build a simple machine learning model to make predictions, then we write the prediction results back into the database.
Summary
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| Main Code: 690 LOC (2 files) = YAML (100%) Secondary code: Test: 0 LOC (0); Generated: 0 LOC (0); Build & Deploy: 0 LOC (0); Other: 50 LOC (3); |
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| Duplication: 93% | |||
| File Size: 0% long (>1000 LOC), 0% short (<= 200 LOC) | |||
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Logical Component Decomposition: primary (2 components) | ||
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1 year, 2 months old
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0% of code updated more than 50 times Also see temporal dependencies for files frequently changed in same commits. |
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| Goals: Keep the system simple and easy to change (4) | |||
Latest commit date: 2021-09-19
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