Main features: End-to-end machine learning pipeline, data processing components, machine learning algorithms.
Objectives: To increase the efficiency of ML models, to achieve near-optimal performance in data/concept drift detection tasks, in next generation 6G networks, to reduce energy consumption during the model retraining operations.
What is new: Existing methodologies consider only a few factors to assess whether an AI model be retrained or not, given new input data. NANCY’s data drift and concept drift detectors employ a complex yet highly computational efficient mechanism to integrate several parameters into the mix. This highly increases the assessment accuracy and perfectly situates the result under investigation to be used in production environments.
Why is it important: This exploitable result does not only advance the state-of-the-art of the AI models for data drift and concept drift detection, but also it provides a real solution for next generation 5G/6G networks. The algorithms are designed to achieve near-optimal performance in real-world scenarios with real-world data.”
