# Cost-Effective Retraining of Machine Learning Models

arXiv: https://arxiv.org/abs/2310.04216 · HTML: https://mlsanity.com/paper/2310.04216/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 20 references that are in the map.
- First posted on arXiv 6 Oct 2023.

## Builds on (most cited first, 2 of 2)

- [Scikit-Multiflow: A Multi-output Streaming Framework](https://mlsanity.com/paper/1807.04662/) (2018)
- [Reactive Soft Prototype Computing for Concept Drift Streams](https://mlsanity.com/paper/2007.05432/) (2020)

## Built on by (most cited first, 7 of 7)

- [Provable unlearning in topic modeling and downstream tasks](https://mlsanity.com/paper/2411.12600/) (2024)
- [Towards Stable Machine Learning Model Retraining via Slowly Varying Sequences](https://mlsanity.com/paper/2403.19871/) (2024)
- [Solve Smart, Not Often: Policy Learning for Costly MILP Re-solving](https://mlsanity.com/paper/2509.23470/) (2025)
- [Bayesian-based Online Label Shift Estimation with Dynamic Dirichlet Priors](https://mlsanity.com/paper/2511.18615/) (2025)
- [Optimal Resource Allocation for ML Model Training and Deployment under Concept Drift](https://mlsanity.com/paper/2512.12816/) (2025)
- [TIMEGATE: Sustainable Time-Boxed Promotion Gates for Continual ML Adaptation Under Resource Constraints](https://mlsanity.com/paper/2605.29183/) (2026)
- [Drift Happens: An Empirical Study of Neural Architecture Robustness to Temporal Distribution Shift](https://mlsanity.com/paper/2607.05908/) (2026)

## Abstract (arXiv)

It is important to retrain a machine learning (ML) model in order to maintain its performance as the data changes over time. However, this can be costly as it usually requires processing the entire dataset again. This creates a trade-off between retraining too frequently, which leads to unnecessary computing costs, and not retraining often enough, which results in stale and inaccurate ML models. To address this challenge, we propose ML systems that make automated and cost-effective decisions about when to retrain an ML model. We aim to optimize the trade-off by considering the costs associated with each decision. Our research focuses on determining whether to retrain or keep an existing ML model based on various factors, including the data, the model, and the predictive queries answered by the model. Our main contribution is a Cost-Aware Retraining Algorithm called Cara, which optimizes the trade-off over streams of data and queries. To evaluate the performance of Cara, we analyzed synthetic datasets and demonstrated that Cara can adapt to different data drifts and retraining costs while performing similarly to an optimal retrospective algorithm. We also conducted experiments with real-world datasets and showed that Cara achieves better accuracy than drift detection baselines while making fewer retraining decisions, ultimately resulting in lower total costs.