aws-samples / amazon-sagemaker-drift-detection
This sample demonstrates how to setup an Amazon SageMaker MLOps end-to-end pipeline for Drift detection
Summary
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| Main Code: 4,249 LOC (33 files) = PY (90%) + YML (9%) Secondary code: Test: 275 LOC (1); Generated: 1,352 LOC (1); Build & Deploy: 11 LOC (1); Other: 4,296 LOC (20); |
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| Duplication: 37% | |||
| File Size: 0% long (>1000 LOC), 43% short (<= 200 LOC) | |||
| Unit Size: 0% long (>100 LOC), 76% short (<= 10 LOC) | |||
| Conditional Complexity: 0% complex (McCabe index > 50), 87% simple (McCabe index <= 5) | |||
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Logical Component Decomposition: primary (11 components) | ||
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6 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) | |||
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Features of interest:
TODOs
1 file |
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Latest commit date: 2021-12-22
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