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Show HN: Graphsignal – Production Model Monitoring: Anomalies, Drift, Exceptions
- FionnMc 5y agoWow! A great idea (haven't look into the code yet). With the new EU AI regulations coming in 2023/4. Every company with ML in production will need to be able to monitor these issues. Potential for a very good open core business model.
- citilife 5y agoIt 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.