2 citations · 4 across the 6 of their papers we have counts for
6 papers
End-to-End Data Quality-Driven Framework for Machine Learning in Production Environment
Firas Bayram, Bestoun S. Ahmed, Erik Hallin
This paper introduces a novel end-to-end framework that efficiently integrates data quality assessment with machine learning (ML) model operations in real-time production environme…
Adaptive Data Quality Scoring Operations Framework using Drift-Aware Mechanism for Industrial Applications
Firas Bayram, Bestoun S. Ahmed, Erik Hallin
Within data-driven artificial intelligence (AI) systems for industrial applications, ensuring the reliability of the incoming data streams is an integral part of trustworthy decisi…
DQSOps: Data Quality Scoring Operations Framework for Data-Driven Applications
Firas Bayram, Bestoun S. Ahmed, Erik Hallin +1
Data quality assessment has become a prominent component in the successful execution of complex data-driven artificial intelligence (AI) software systems. In practice, real-world a…
Quality Assurance in MLOps Setting: An Industrial Perspective
Ayan Chatterjee, Bestoun S. Ahmed, Erik Hallin +1
Today, machine learning (ML) is widely used in industry to provide the core functionality of production systems. However, it is practically always used in production systems as par…
A Drift Handling Approach for Self-Adaptive ML Software in Scalable Industrial Processes
Firas Bayram, Bestoun S. Ahmed, Erik Hallin +1
Most industrial processes in real-world manufacturing applications are characterized by the scalability property, which requires an automated strategy to self-adapt machine learnin…
Testing of Machine Learning Models with Limited Samples: An Industrial Vacuum Pumping Application
Ayan Chatterjee, Bestoun S. Ahmed, Erik Hallin +1
There is often a scarcity of training data for machine learning (ML) classification and regression models in industrial production, especially for time-consuming or sparsely run ma…