Monitoring and explainability of models in production

可解释与可解释的机器学习方法用于预测性流程监控：一项系统文献综述

Interpretable and Explainable Machine Learning Methods for Predictive Process Monitoring: A Systematic Literature Review

本文通过系统文献综述，探讨预测性流程挖掘中机器学习模型的可解释性与可解释性，区分内在可解释模型与事后解释技术，并总结趋势、挑战与未来方向，以促进更可信的智能系统开发。

可解释提升机与稀疏性——在高维设置中保持可解释性

Explainable Boosting Machines with Sparsity -- Maintaining Explainability in High-Dimensional Settings

针对高维场景下可解释提升机可解释性下降和评分变慢的问题，提出基于LASSO的后处理稀疏化方法，通过重加权并移除不相关项来保持透明性并大幅提升评分速度。

Optimal Probing with Statistical Guarantees for Network Monitoring at Scale

Cloud networks are difficult to monitor because they grow rapidly and the budgets for monitoring them are limited. We propose a framework for estimating network metrics, such as latency and packet loss, with guarantees on estimation errors for a fixed monitoring budget. Our proposed algorithms produce a distribution of probes across network paths, which we then monitor; and are based on A- and E-optimal experimental designs in statistics. Unfortunately, these designs are too computationally costly to use at production scale. We propose their scalable and near-optimal approximations based on th…

dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python

The increasing amount of available data, computing power, and the constant pursuit for higher performance results in the growing complexity of predictive models. Their black-box nature leads to opaqueness debt phenomenon inflicting increased risks of discrimination, lack of reproducibility, and deflated performance due to data drift. To manage these risks, good MLOps practices ask for better validation of model performance and fairness, higher explainability, and continuous monitoring. The necessity of deeper model transparency appears not only from scientific and social domains, but also emer…

ML Health: Fitness Tracking for Production Models

Deployment of machine learning (ML) algorithms in production for extended periods of time has uncovered new challenges such as monitoring and management of real-time prediction quality of a model in the absence of labels. However, such tracking is imperative to prevent catastrophic business outcomes resulting from incorrect predictions. The scale of these deployments makes manual monitoring prohibitive, making automated techniques to track and raise alerts imperative. We present a framework, ML Health, for tracking potential drops in the predictive performance of ML models in the absence of la…

SafeML: Safety Monitoring of Machine Learning Classifiers through Statistical Difference Measure

Ensuring safety and explainability of machine learning (ML) is a topic of increasing relevance as data-driven applications venture into safety-critical application domains, traditionally committed to high safety standards that are not satisfied with an exclusive testing approach of otherwise inaccessible black-box systems. Especially the interaction between safety and security is a central challenge, as security violations can lead to compromised safety. The contribution of this paper to addressing both safety and security within a single concept of protection applicable during the operation o…