4 citations · 9 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach
Firas Bayram, Bestoun S. Ahmed
Artificial intelligence (AI), and especially its sub-field of Machine Learning (ML), are impacting the daily lives of everyone with their ubiquitous applications. In recent years,…
DA-LSTM: A Dynamic Drift-Adaptive Learning Framework for Interval Load Forecasting with LSTM Networks
Firas Bayram, Phil Aupke, Bestoun S. Ahmed +3
Load forecasting is a crucial topic in energy management systems (EMS) due to its vital role in optimizing energy scheduling and enabling more flexible and intelligent power grid s…
A Domain-Region Based Evaluation of ML Performance Robustness to Covariate Shift
Firas Bayram, Bestoun S. Ahmed
Most machine learning methods assume that the input data distribution is the same in the training and testing phases. However, in practice, this stationarity is usually not met and…
From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors
Firas Bayram, Bestoun S. Ahmed, Andreas Kassler
The dynamicity of real-world systems poses a significant challenge to deployed predictive machine learning (ML) models. Changes in the system on which the ML model has been trained…