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It would be possible to build a similar system via a library my team has built: https://github.com/capitalone/dataprofiler https://github.com/capitalone/datapro
by citilife 5y ago
It would be possible to build a similar system via a library my team has built: https://github.com/capitalone/dataprofiler https://github.com/capitalone/dataprofiler
Effectively, you can monitor changes between profiles:
data1 = dp.Data("file_a.csv") # Load a CSV file
profile1 = dp.Profiler(data1) # Generate a profile
data2 = dp.Data("file_b.csv") # Load another CSV file
profile2 = dp.Profiler(data2) # Generate another profile
diff_report = profile1.diff(profile2)
print(json.dumps(diff_report, indent=4))
The system we have generates reports, it might be worth adding it OP.
- manojlds 5y agoWhat does this have to do with model monitoring?
- citilife 5y agoYou can pass the output of the model to the profiling system to monitor if things are drifting. It's also possible to monitor the input data and link back. There's quite a few ways to do this, but effectively you can monitor drift by identifying which inputs have the greatest impact in accuracy. Then tying that back to predict the drift over time.