A Multi-Criteria Automated MLOps Pipeline for Cost-Effective Cloud-Based Classifier Retraining in Response to Data Distribution Shifts
Fuente:
arXiv
Saved in:
| Main Authors: | Katalay, Emmanuel K., Dimandja, David O., Masakuna, Jordan F. |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
DNN-Powered MLOps Pipeline Optimization for Large Language Models: A Framework for Automated Deployment and Resource Management
by: Krishnamoorthy, Mahesh Vaijainthymala, et al.
Published: (2025)
by: Krishnamoorthy, Mahesh Vaijainthymala, et al.
Published: (2025)
On the Unreasonable Effectiveness of Last-layer Retraining
by: Hill, John C., et al.
Published: (2025)
by: Hill, John C., et al.
Published: (2025)
Taming Hyperparameter Sensitivity in Data Attribution: Practical Selection Without Costly Retraining
by: Wang, Weiyi, et al.
Published: (2025)
by: Wang, Weiyi, et al.
Published: (2025)
Enhanced Pruning for Distributed Closeness Centrality under Multi-Packet Messaging
by: Manya, Patrick D., et al.
Published: (2025)
by: Manya, Patrick D., et al.
Published: (2025)
A Five-Layer MLOps Architecture for Connected Automated Driving
by: Lampe, Bastian, et al.
Published: (2026)
by: Lampe, Bastian, et al.
Published: (2026)
SmartMLOps Studio: Design of an LLM-Integrated IDE with Automated MLOps Pipelines for Model Development and Monitoring
by: Jin, Jiawei, et al.
Published: (2025)
by: Jin, Jiawei, et al.
Published: (2025)
Automating Code Adaptation for MLOps -- A Benchmarking Study on LLMs
by: Patel, Harsh, et al.
Published: (2024)
by: Patel, Harsh, et al.
Published: (2024)
Automating the Training and Deployment of Models in MLOps by Integrating Systems with Machine Learning
by: Liang, Penghao, et al.
Published: (2024)
by: Liang, Penghao, et al.
Published: (2024)
Impact of Inaccurate Contamination Ratio on Robust Unsupervised Anomaly Detection
by: Masakuna, Jordan F., et al.
Published: (2024)
by: Masakuna, Jordan F., et al.
Published: (2024)
CEBench: A Benchmarking Toolkit for the Cost-Effectiveness of LLM Pipelines
by: Sun, Wenbo, et al.
Published: (2024)
by: Sun, Wenbo, et al.
Published: (2024)
Deep Learning for Network Anomaly Detection under Data Contamination: Evaluating Robustness and Mitigating Performance Degradation
by: Nkashama, D'Jeff K., et al.
Published: (2024)
by: Nkashama, D'Jeff K., et al.
Published: (2024)
Automating Data Science Pipelines with Tensor Completion
by: Pakala, Shaan, et al.
Published: (2024)
by: Pakala, Shaan, et al.
Published: (2024)
On the Stability of Iterative Retraining of Generative Models on their own Data
by: Bertrand, Quentin, et al.
Published: (2023)
by: Bertrand, Quentin, et al.
Published: (2023)
Enhancing Cell Counting through MLOps: A Structured Approach for Automated Cell Analysis
by: Testi, Matteo, et al.
Published: (2025)
by: Testi, Matteo, et al.
Published: (2025)
Governing Cloud Data Pipelines with Agentic AI
by: Kirubakaran, Aswathnarayan Muthukrishnan, et al.
Published: (2025)
by: Kirubakaran, Aswathnarayan Muthukrishnan, et al.
Published: (2025)
An Efficient Model Maintenance Approach for MLOps
by: Majidi, Forough, et al.
Published: (2024)
by: Majidi, Forough, et al.
Published: (2024)
Embedding the MLOps Lifecycle into OT Reference Models
by: Schindler, Simon, et al.
Published: (2025)
by: Schindler, Simon, et al.
Published: (2025)
Did US Worker Retraining Reduce Participant Automation Exposure?
by: Jacobs, Julian, et al.
Published: (2026)
by: Jacobs, Julian, et al.
Published: (2026)
The FreshPRINCE: A Simple Transformation Based Pipeline Time Series Classifier
by: Middlehurst, Matthew, et al.
Published: (2022)
by: Middlehurst, Matthew, et al.
Published: (2022)
Benchmarking Distribution Shift in Tabular Data with TableShift
by: Gardner, Josh, et al.
Published: (2023)
by: Gardner, Josh, et al.
Published: (2023)
When Benchmarks Lie: Evaluating Malicious Prompt Classifiers Under True Distribution Shift
by: Fomin, Max
Published: (2026)
by: Fomin, Max
Published: (2026)
Linear Mode Connectivity under Data Shifts for Deep Ensembles of Image Classifiers
by: Hepburn, C., et al.
Published: (2025)
by: Hepburn, C., et al.
Published: (2025)
Ethical and Explainable AI in Reusable MLOps Pipelines
by: Hossain, Rakib, et al.
Published: (2026)
by: Hossain, Rakib, et al.
Published: (2026)
On the Impossibility of Retrain Equivalence in Machine Unlearning
by: Yu, Jiatong, et al.
Published: (2025)
by: Yu, Jiatong, et al.
Published: (2025)
Harmonica: A Self-Adaptation Exemplar for Sustainable MLOps
by: Halgatti, Ananya, et al.
Published: (2026)
by: Halgatti, Ananya, et al.
Published: (2026)
Data Distribution Shifts in (Industrial) Federated Learning as a Privacy Issue
by: Brunner, David, et al.
Published: (2024)
by: Brunner, David, et al.
Published: (2024)
Optimising Process Automation of Geospatial Data Pipelines by Artificial Intelligence
by: Lemenkova, Polina
Published: (2025)
by: Lemenkova, Polina
Published: (2025)
Stratos: An End-to-End Distillation Pipeline for Customized LLMs under Distributed Cloud Environments
by: Dai, Ziming, et al.
Published: (2025)
by: Dai, Ziming, et al.
Published: (2025)
Theoretical Guarantees of Data Augmented Last Layer Retraining Methods
by: Welfert, Monica, et al.
Published: (2024)
by: Welfert, Monica, et al.
Published: (2024)
When to Retrain after Drift: A Data-Only Test of Post-Drift Data Size Sufficiency
by: Fujiwara, Ren, et al.
Published: (2026)
by: Fujiwara, Ren, et al.
Published: (2026)
Instance-Level Costs for Nuanced Classifier Evaluation
by: Kang, Kabir, et al.
Published: (2026)
by: Kang, Kabir, et al.
Published: (2026)
Graph Unlearning with Efficient Partial Retraining
by: Zhang, Jiahao, et al.
Published: (2024)
by: Zhang, Jiahao, et al.
Published: (2024)
Experimentation, deployment and monitoring Machine Learning models: Approaches for applying MLOps
by: Nogare, Diego, et al.
Published: (2024)
by: Nogare, Diego, et al.
Published: (2024)
Reconstruct the Pruned Model without Any Retraining
by: Wang, Pingjie, et al.
Published: (2024)
by: Wang, Pingjie, et al.
Published: (2024)
Rethinking Explanation Evaluation under the Retraining Scheme
by: Cai, Yi, et al.
Published: (2025)
by: Cai, Yi, et al.
Published: (2025)
Enabling Adversarial Robustness in AI Models through Kubeflow MLOps
by: Bouras, Stavros, et al.
Published: (2026)
by: Bouras, Stavros, et al.
Published: (2026)
MLE-Smith: Scaling MLE Tasks with Automated Multi-Agent Pipeline
by: Qiang, Rushi, et al.
Published: (2025)
by: Qiang, Rushi, et al.
Published: (2025)
Quantifying Uncertainty in the Presence of Distribution Shifts
by: Slavutsky, Yuli, et al.
Published: (2025)
by: Slavutsky, Yuli, et al.
Published: (2025)
Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining
by: Alçalar, Yaşar Utku, et al.
Published: (2025)
by: Alçalar, Yaşar Utku, et al.
Published: (2025)
Design and Implementation of an MLOps Pipeline for Audio-Based Crowd Sentiment Analysis in Stadiums
by: Isselkou, Ahmed
Published: (2025)
by: Isselkou, Ahmed
Published: (2025)
Similar Items
-
DNN-Powered MLOps Pipeline Optimization for Large Language Models: A Framework for Automated Deployment and Resource Management
by: Krishnamoorthy, Mahesh Vaijainthymala, et al.
Published: (2025) -
On the Unreasonable Effectiveness of Last-layer Retraining
by: Hill, John C., et al.
Published: (2025) -
Taming Hyperparameter Sensitivity in Data Attribution: Practical Selection Without Costly Retraining
by: Wang, Weiyi, et al.
Published: (2025) -
Enhanced Pruning for Distributed Closeness Centrality under Multi-Packet Messaging
by: Manya, Patrick D., et al.
Published: (2025) -
A Five-Layer MLOps Architecture for Connected Automated Driving
by: Lampe, Bastian, et al.
Published: (2026)