Big Data Assurance: An Approach Based on Service-Level Agreements

Ardagna, Claudio Agostino; Bena, Nicola; Hebert, Cédric; Krotsiani, Maria; Kloukinas, Christos; Spanoudakis, George

doi:10.1089/big.2021.0369

Cited by 6 publications

(1 citation statement)

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“…This information has been proven to be crucial for future development and testing endeavors. Performance tests also evaluate the model's response time under normal and peak loads, ensuring adherence to the Service Level Agreement (SLA) [190] and preventing compromise in availability due to request timeouts. Collecting statistics on the ML model's responsiveness under varying workloads aids in identifying potential issues and guarantees that the system performs within specified operational parameters.…”

Section: Performance Testingmentioning

confidence: 99%

Fundamental Components and Principles of Supervised Machine Learning Workflows with Numerical and Categorical Data

Kampezidou,

Tikayat Ray,

Bhat

et al. 2024

Eng

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This paper offers a comprehensive examination of the process involved in developing and automating supervised end-to-end machine learning workflows for forecasting and classification purposes. It offers a complete overview of the components (i.e., feature engineering and model selection), principles (i.e., bias–variance decomposition, model complexity, overfitting, model sensitivity to feature assumptions and scaling, and output interpretability), models (i.e., neural networks and regression models), methods (i.e., cross-validation and data augmentation), metrics (i.e., Mean Squared Error and F1-score) and tools that rule most supervised learning applications with numerical and categorical data, as well as their integration, automation, and deployment. The end goal and contribution of this paper is the education and guidance of the non-AI expert academic community regarding complete and rigorous machine learning workflows and data science practices, from problem scoping to design and state-of-the-art automation tools, including basic principles and reasoning in the choice of methods. The paper delves into the critical stages of supervised machine learning workflow development, many of which are often omitted by researchers, and covers foundational concepts essential for understanding and optimizing a functional machine learning workflow, thereby offering a holistic view of task-specific application development for applied researchers who are non-AI experts. This paper may be of significant value to academic researchers developing and prototyping machine learning workflows for their own research or as customer-tailored solutions for government and industry partners.

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Section: Performance Testingmentioning

confidence: 99%