Towards Continuous Experiment-driven MLOps

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Towards Continuous Experiment-driven MLOps

Popis výsledku v původním jazyce

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).

Název v anglickém jazyce

Towards Continuous Experiment-driven MLOps

Popis výsledku anglicky

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).

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2025 IEEE/ACM 4TH INTERNATIONAL CONFERENCE ON AI ENGINEERING-SOFTWARE ENGINEERING FOR AI, CAIN

ISBN

979-8-3315-0220-1

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

89-94

Název nakladatele

IEEE COMPUTER SOC

Místo vydání

LOS ALAMITOS

Místo konání akce

Ottawa

Datum konání akce

27. 4. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001547268600010