popmon: population shift monitoring made easy

Tracking model performance is crucial to guarantee that a model behaves as designed initially. Predictions may be way ahead in time, so the performance can only be verified later, for example, in one year. Taking actions at that point might be already too late. Typical model performance questions are: is the model performing as expected, and are predictions made on current incoming data still valid?

Model performance depends directly on the data used for training and the data to make predictions. Changes in the latter (e.g. certain word frequency, user demographics, etc.) can affect the performance and make predictions unreliable. Given that input data often change over time, it is important to track changes in both input distributions and delivered predictions periodically, and when they differ significantly - take actions. For example, diagnose and retrain an incorrect model in production.

To make monitoring both more consistent and semi-automatic, at ING we have developed a generic Python package called "popmon" to monitor the stability of data populations over time, using techniques from statistical process control. In this talk, the speaker will present multiple scenarios of population shift, the motivation and challenges of population monitoring, as well as our open-source solution to these.

Transcript

1. ` ` population shi monitoring made easy Tomas Sostak 16 June 2020 @tomassostak ING Wholesale Banking Advanced Analytics
2. PRESENTATION OUTLINE • MOTIVATION • PROBLEMS • POPMON SOLUTION • DEMO • USE CASE About me Tomas Sostak Data Scientist @ ING Wholesale Banking Advanced Analytics @tomassostak
3. Our why - Our ML models and data were not being monitored carefully enough - No good open-source solution available - Past experience in doing this right
4. Motive ‣ Running reliable and consistent models in production ‣ Are newly incoming data/predictions consistent with the historical data on which model has been trained and tested on initially? ‣ If input features change, then tested performance is not guaranteed ‣ Full control of continuous retraining of deployed models ‣ Think twice before retraining your model if new data has different distribution than the old one ‣ Reporting - audit, paper trail
5. Common Issues Population Shift Data Rot Time Dependencies
6. Monitoring data predictions model performance
7. Monitoring data predictions model performance
8. data profile data points histogram Population shi
9. https://deepai.org/publication/conformal-prediction-under-covariate-shift https://www.researchgate.net/figure/Covariate-shift-Training-and-test-data-sets-are-drawn-from-different-distributions_fig24_330485084 Population shi
10. Steps
11. New data Steps
12. New data New (test) Histograms Steps
13. New data Reference (train) New (test) Histograms Comparison Steps
14. New data Reference (train) New (test) Histograms Comparison Metrics Steps
15. New data Reference (train) New (test) Histograms Comparison Metrics Thresholds Steps
16. New data Reference (train) New (test) Histograms Comparison Metrics Alerting Thresholds Steps
17. 1 year of data 52,000 26,000 0 A B C D
18. 1 year of data 52,000 26,000 0 A B C D Week 1 1,000 500 0 A B C D Week 2 1,000 500 0 A B C D Week 3 Week 52 …
19. Week 1 1,000 500 0 A B C D
20. 1,000 500 0 A B C D Week 2
21. Overlay: week 1 vs 2 1,000 500 0 A B C D
22. Overlay: week 1 vs 2 1,000 500 0 A B C D
23. Statistical tests 1,000 500 0 A B C D • Chi-squared • Kolmogorov-Smirnov • Pearson’s correlation • Your own tests
24. WHY HISTOGRAMS? • Aggregated information (data privacy) • Size = easy to store, light for sending over APIs • Monitoring works identically with both big and small data • More visual - adds information (distribution) • Useful for applying all sorts of statistical tests
25. Prediction monitoring
26. DS & DE - Great for data exploration (seeing data patterns, trend, seasonality, outliers). - Very valuable for early inspection of covariate shifts - Data ingestion pipelines (monitor your incoming data to prevent drop in performance); stitching is available (e.g. data coming in batches: over certain period, or a number records)
27. Profiling Reference points Statistical comparisons
28. Profiling Statistical comparisons Reference points count, mean, std, filled, nan, min, max, p01, p05, p25, p50, p75, p95, p99,… • Self • Reference (train) • Rolling (sliding) • Expanding • Chi-squared • Kolmogorov-Smirnov • Pearson’s correlation • Trend detection (LR) • Custom tests
29. Tra ic lights
30. Tra ic lights Standard score: We set configurable bounds: (-2, -1, 1, 2)
31. Tra ic lights Week 1 1,000 500 0 A B C D mean
32. Tra ic lights Get distribution of reference data over time
33. Tra ic lights
34. Tra ic lights Set traffic light bounds
35. - Use popmon to monitor the stability of a pandas or spark dataset - Automatically detect changes over time: trends, shifts, peaks, outliers, anomalies, changing correlations, etc. - Alerting based on static or dynamic business rules. - Easy to extend: make your own data pipelines (with preferred configurations) + your own implemented statistical tests = it will all automatically show up in the report - Supports 1D & 2D histograms released April 2020
38. demo
40. Internal use case time Chi2
41. time Chi2 Internal use case
42. time Chi2 - Switch to a new data source - Training on all data: - AUC model performance: 0.972 - Training on the new data only: - AUC model performance: 0.995 Internal use case
43. Thank you! https://github.com/ing-bank/popmon pip install popmon Are you ready to give your data the attention it deserves?