Towards Continuous Experiment-driven MLOps
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A10507956" target="_blank" >RIV/00216208:11320/25:10507956 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/CAIN66642.2025.00018" target="_blank" >https://doi.org/10.1109/CAIN66642.2025.00018</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CAIN66642.2025.00018" target="_blank" >10.1109/CAIN66642.2025.00018</a>
Alternative languages
Result language
angličtina
Original language name
Towards Continuous Experiment-driven MLOps
Original language description
Despite advancements in MLOps and AutoML, ML development still remains challenging for data scientists. First, there is poor support for and limited control over optimizing and evolving ML models. Second, there is lack of efficient mechanisms for continuous evolution of ML models which would leverage the knowledge gained in previous optimizations of the same or different models. We propose an experiment-driven MLOps approach which tackles these problems. Our approach relies on the concept of an experiment, which embodies a fully controllable optimization process. It introduces full traceability and repeatability to the optimization process, allows humans to be in full control of it, and enables continuous improvement of the ML system. Importantly, it also establishes knowledge, which is carried over and built across a series of experiments and allows for improving the efficiency of experimentation over time. We demonstrate our approach through its realization and application in the ExtremeXP(1) project (Horizon Europe).
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2025 IEEE/ACM 4TH INTERNATIONAL CONFERENCE ON AI ENGINEERING-SOFTWARE ENGINEERING FOR AI, CAIN
ISBN
979-8-3315-0220-1
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
89-94
Publisher name
IEEE COMPUTER SOC
Place of publication
LOS ALAMITOS
Event location
Ottawa
Event date
Apr 27, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001547268600010