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Modeling and Controlling Deployment Reliability under Temporal Distribution Shift

Naimur Rahman, Naazreen Tabassum

Abstract

Machine learning models deployed in non-stationary environments are exposed to temporal distribution shift, which can erode predictive reliability over time. While common mitigation strategies such as periodic retraining and recalibration aim to preserve performance, they typically focus on average metrics evaluated at isolated time points and do not explicitly model how reliability evolves during deployment. We propose a deployment-centric framework that treats reliability as a dynamic state composed of discrimination and calibration. The trajectory of this state across sequential evaluation windows induces a measurable notion of volatility, allowing deployment adaptation to be formulated as a multi-objective control problem that balances reliability stability against cumulative intervention cost. Within this framework, we define a family of state-dependent intervention policies and empirically characterize the resulting cost-volatility Pareto frontier. Experiments on a large-scale, temporally indexed credit-risk dataset (1.35M loans, 2007-2018) show that selective, drift-triggered interventions can achieve smoother reliability trajectories than continuous rolling retraining while substantially reducing operational cost. These findings position deployment reliability under temporal shift as a controllable multi-objective system and highlight the role of policy design in shaping stability-cost trade-offs in high-stakes tabular applications.

Modeling and Controlling Deployment Reliability under Temporal Distribution Shift

Abstract

Machine learning models deployed in non-stationary environments are exposed to temporal distribution shift, which can erode predictive reliability over time. While common mitigation strategies such as periodic retraining and recalibration aim to preserve performance, they typically focus on average metrics evaluated at isolated time points and do not explicitly model how reliability evolves during deployment. We propose a deployment-centric framework that treats reliability as a dynamic state composed of discrimination and calibration. The trajectory of this state across sequential evaluation windows induces a measurable notion of volatility, allowing deployment adaptation to be formulated as a multi-objective control problem that balances reliability stability against cumulative intervention cost. Within this framework, we define a family of state-dependent intervention policies and empirically characterize the resulting cost-volatility Pareto frontier. Experiments on a large-scale, temporally indexed credit-risk dataset (1.35M loans, 2007-2018) show that selective, drift-triggered interventions can achieve smoother reliability trajectories than continuous rolling retraining while substantially reducing operational cost. These findings position deployment reliability under temporal shift as a controllable multi-objective system and highlight the role of policy design in shaping stability-cost trade-offs in high-stakes tabular applications.

Paper Structure

This paper contains 67 sections, 14 equations, 4 figures, 7 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Temporal Dataset Shift and Concept Drift
  4. Mitigation Strategies under Shift
  5. Drift Detection and Monitoring
  6. Multi-Objective Control for Deployment Reliability
  7. Problem Formulation
  8. Deployment Setting
  9. Reliability as a Dynamic State
  10. Intervention Policies and Cost
  11. Multi-Objective Deployment Problem
  12. Method
  13. From Temporal Evaluation to Deployment Policies
  14. Action semantics.
  15. Cost model abstraction.
  16. ...and 52 more sections

Figures (4)

  • Figure 1: ROC AUC across deployment windows. Rolling retraining yields the highest average discrimination but exhibits noticeable fluctuations. DTRC maintains intermediate discrimination while avoiding extreme swings.
  • Figure 2: Expected Calibration Error (ECE) across deployment windows. Periodic recalibration reduces calibration error on average but introduces oscillatory behaviour across windows; DTRC stabilizes calibration without continuous updates.
  • Figure 3: Drift signal over time summarizing distributional change between evaluation windows. Periods of elevated drift (e.g., mid-horizon years) coincide with higher reliability volatility under static deployment. DTRC activates selectively during these intervals, smoothing subsequent trajectory evolution.
  • Figure 4: Empirical Pareto frontier in cost--volatility space for MORC policies. Points represent distinct threshold configurations; the highlighted point corresponds to the knee configuration used for DTRC.