Research
On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

Trend · papers per month

count% of corpus
0.3%0.5%0.8%0.1% · Dec 202019922001200920172026
4 results for “MLOps”
CodeReef: an open platform for portable MLOps, reusable automation actions and reproducible benchmarkingcs.LG

CodeReef enables sharing ML models across platforms efficiently.

problem Sharing and deploying ML models across different systems efficiently.
method Developed an open platform to share ML components, automate deployment, and benchmark models.
result Demonstrated efficient deployment and benchmarking of ML models across diverse platforms.
MLOpsAutomationReproducibilityBenchmarkingMLPerf
2020-01-22abs ↗pdf ↗
From XAI to MLOps: Explainable Concept Drift Detection with Profile Drift Detectionstat.ML

PDD detects concept drift using explainable AI, improving model performance in dynamic environments.

problem Detecting and adapting to concept drift in predictive models.
method Profile Drift Detection (PDD) using Partial Dependence Profiles (PDPs).
result PDD outperforms existing methods in detecting concept drift and maintaining high predictive performance.
concept driftexplainable AIPartial Dependence ProfilesMLOpsdata drift
2024-12-15abs ↗pdf ↗
dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Pythoncs.LG

dalex simplifies model exploration and fairness for Python developers.

problem Model black-box nature and risks of discrimination, lack of reproducibility, and data drift.
method Model-agnostic interface for interactive model exploration.
result Enhances model transparency and accountability through interactive explainability and fairness.
machine learningexplainabilityfairnessMLOpsPython
2020-12-28abs ↗pdf ↗
The Collective Knowledge project: making ML models more portable and reproducible with open APIs, reusable best practices and MLOpscs.LG

CK simplifies ML model deployment and reproducibility with open APIs and DevOps.

problem Making ML models reproducible and deployable across different environments.
method Decompose complex systems into reusable sub-components with unified APIs and DevOps principles.
result Automatically co-design and optimize ML models for speed, accuracy, energy, and size.
Machine LearningModel ReproducibilityDevOpsOpen APIsSoftware Engineering
2020-06-12abs ↗pdf ↗