awslabs / predictive-maintenance-using-machine-learning
Set up end-to-end demo architecture for predictive maintenance issues with Machine Learning using Amazon SageMaker
Summary
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| Main Code: 125,427 LOC (451 files) = PY (93%) + H (4%) + C (<1%) + YAML (<1%) + TPL (<1%) + IN (<1%) Secondary code: Test: 220,304 LOC (718); Generated: 0 LOC (0); Build & Deploy: 66 LOC (3); Other: 1,828 LOC (13); |
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| File Size: 37% long (>1000 LOC), 12% short (<= 200 LOC) | |||
| Unit Size: 7% long (>100 LOC), 47% short (<= 10 LOC) | |||
| Conditional Complexity: 11% complex (McCabe index > 50), 41% simple (McCabe index <= 5) | |||
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Logical Component Decomposition: primary (32 components) | ||
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2 years, 7 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
94 files |
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Latest commit date: 2021-05-18
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