Poster: Re-Testing Configured Instances in the Production Environment - A Method for Reducing the Test Suite

Jebbar, Oussama; Saied, Mohamed Aymen; Khendek, Ferhat; Toeroe, Maria

doi:10.1109/icst.2019.00044

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…There are often three deployment instances used when building applications. These are: development, QA, and production [46,47]. Code integrity maintenance is an important function of DevOps build systems.…”

Section: Buildmentioning

confidence: 99%

MLOps critical success factors - A systematic literature review

Mehmood,

Sabahat,

Muhammad Arsal Ijaz

2024

VFAST trans. softw. eng.

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MLOps encompasses a collection of practices integrating machine learning into operational activities, a recent addition to the diverse array of machine learning process models. The need to tightly integrate machine learning with information systems operations to ensure organizational performance led to the development of this approach. Therefore, MLOps methodologies are useful for businesses that want to make their ML operations and procedures more efficient. The purpose of this study is to summarize the many critical success factors that have been identified in studies focusing on MLOps initiatives. The paper shows how these CSFs affect MLOps performance and what factors drive this influence. We picked primary papers for analysis after conducting searches in three major publishing databases. We narrowed the field down to 58 unique CSFs, which were then classified according to three dimensions: technical, organizational, social and cultural. These CSFs affect and drive performance in MLOps, based on the results of the literature review. Researchers and industrial experts may enhance their understanding of CSFs and get insights into tackling MLOps difficulties inside organizations. The paper, notably, emphasizes several prospective research directions linked to CSFs.

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Section: Buildmentioning

confidence: 99%

MLOps critical success factors - A systematic literature review

Mehmood,

Sabahat,

Muhammad Arsal Ijaz

2024

VFAST trans. softw. eng.

View full text Add to dashboard Cite

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Regression Test Suite Reduction for Cloud Systems

Jebbar

Saied

Khendek

et al. 2020

2020 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)

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From DevOps to MLOps: Overview and Application to Electricity Market Forecasting

2022

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In the Software Development Life Cycle (SDLC), Development and Operations (DevOps) has been proven to deliver reliable, scalable software within a shorter time. Due to the explosion of Machine Learning (ML) applications, the term Machine Learning Operations (MLOps) has gained significant interest among ML practitioners. This paper explains the DevOps and MLOps processes relevant to the implementation of MLOps. The contribution of this paper towards the MLOps framework is threefold: First, we review the state of the art in MLOps by analyzing the related work in MLOps. Second, we present an overview of the leading DevOps principles relevant to MLOps. Third, we derive an MLOps framework from the MLOps theory and apply it to a time-series forecasting application in the hourly day-ahead electricity market. The paper concludes with how MLOps could be generalized and applied to two more use cases with minor changes.

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Poster: Re-Testing Configured Instances in the Production Environment - A Method for Reducing the Test Suite

Cited by 3 publications

References 8 publications

MLOps critical success factors - A systematic literature review

MLOps critical success factors - A systematic literature review

Regression Test Suite Reduction for Cloud Systems

From DevOps to MLOps: Overview and Application to Electricity Market Forecasting

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