MLOps - Definitions, Tools and Challenges

Symeonidis, Georgios; Nerantzis, Evangelos; Kazakis, Apostolos; Papakostas, George A.

doi:10.1109/ccwc54503.2022.9720902

Cited by 61 publications

(26 citation statements)

References 21 publications

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“…This is to handle the ability to scale out the models as the infrastructure evolves, and, also, to handle the ever-changing ML model for accurate predictions. i.e., MLOps, as presented by Symeonidis et al [ 30 ]. The MLOps uses a collection of tools and processes for the deployment of the ML models into production.…”

Section: Methodsmentioning

confidence: 99%

“…The goal of MLOps is to automate, manage, and speed up the ML model operation by integrating the DevOps process. The maturity level of MLOps implementation is classified into three and five categories by Google (GGL level 0: manual implementation, GGL level 1: an automated pipeline process of building and selecting models but deployment itself remains manual, GGL level 2: a full CI/CD pipeline) and Microsoft (MS level 1: No MLOps, MS level 2: implementation of DevOps but no MLOps, MS level 3: automated training of the model is implemented, MS level 4: the model is deployed autonomously, and MS level 5: the operations are fully through MLOps), respectively, as depicted in Figure 9 and described by Symeonidis et al [ 30 ].…”

Section: Methodsmentioning

confidence: 99%

“…Symeonidis et al [ 30 ] presented three fundamental pipelines: the data manipulation pipeline, model creation pipeline, and the deployment pipeline. Figure 10 presents all these pipelines in the overall MLOps workflow, where planning takes place, followed by data manipulation or management, in which the data is validated, cleaned, labeled, versioned and pre-processed into a database.…”

Section: Methodsmentioning

confidence: 99%

“…Symeonidis et al [ 30 ] concluded that a fully mature MLOps system is the most efficient way to incorporate ML models into production. The MLOps implementation can be challenging, due to the wide variety of tools, limitations, and use-cases for the model.…”

Section: Methodsmentioning

confidence: 99%

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A Study on ML-Based Software Defect Detection for Security Traceability in Smart Healthcare Applications

Mcmurray

Sodhro

2023

Sensors

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Software Defect Prediction (SDP) is an integral aspect of the Software Development Life-Cycle (SDLC). As the prevalence of software systems increases and becomes more integrated into our daily lives, so the complexity of these systems increases the risks of widespread defects. With reliance on these systems increasing, the ability to accurately identify a defective model using Machine Learning (ML) has been overlooked and less addressed. Thus, this article contributes an investigation of various ML techniques for SDP. An investigation, comparative analysis and recommendation of appropriate Feature Extraction (FE) techniques, Principal Component Analysis (PCA), Partial Least Squares Regression (PLS), Feature Selection (FS) techniques, Fisher score, Recursive Feature Elimination (RFE), and Elastic Net are presented. Validation of the following techniques, both separately and in combination with ML algorithms, is performed: Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), K-Nearest Neighbour (KNN), Multilayer Perceptron (MLP), Decision Tree (DT), and ensemble learning methods Bootstrap Aggregation (Bagging), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), Random Forest(RF), and Generalized Stacking (Stacking). Extensive experimental setup was built and the results of the experiments revealed that FE and FS can both positively and negatively affect performance over the base model or Baseline. PLS, both separately and in combination with FS techniques, provides impressive, and the most consistent, improvements, while PCA, in combination with Elastic-Net, shows acceptable improvement.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

A Study on ML-Based Software Defect Detection for Security Traceability in Smart Healthcare Applications

Mcmurray

Sodhro

2023

Sensors

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“…Warnett and Zdun [7] systematically reviewed 35 gray literature articles to identify design decisions for model deployment, where MLOps is a specific design option. Symeonidis et al [13] surveyed the tools that support various tasks in MLOps, such as model deployment, experiment tracking, and feature engineering. They also identified several MLOps challenges, including pipeline development, retraining, and monitoring.…”

Section: Related Workmentioning

confidence: 99%

Requirements and Reference Architecture for MLOps:Insights from Industry

Kumara¹,

Arts²,

Nucci³

et al. 2022

Preprint

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<p>Machine Learning Operations (MLOps) streamline the lifecycle of machine-learning models in production. In recent years, the topic has picked the interest of practitioners, and consequently, a considerable number of tools and gray literature on architecting MLOps environments has emerged. However, this has created a new problem for organizations: selecting the most appropriate tools and design options for implementing their MLOps environments. To alleviate this problem, this paper proposes a reference architecture and requirements for MLOps by systematically reviewing 58 industrial gray literature articles. Such reference architecture drawn from the state of practice shall aid organizations in making better design and technology choices when embarking on their MLOps journey while providing a technology-independent baseline for further MLOps research. </p>

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Applications of MLOps in the Cognitive Cloud Continuum

Moreschini

2022

Product-Focused Software Process Improvement

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MLOps - Definitions, Tools and Challenges

Cited by 61 publications

References 21 publications

A Study on ML-Based Software Defect Detection for Security Traceability in Smart Healthcare Applications

A Study on ML-Based Software Defect Detection for Security Traceability in Smart Healthcare Applications

Requirements and Reference Architecture for MLOps:Insights from Industry

Applications of MLOps in the Cognitive Cloud Continuum

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