DYNAMIC ACCURACY-BASED DEPLOYMENT AND MONITORING OF MACHINE LEARNING MODELS IN PROVIDER NETWORKS

DYNAMIC ACCURACY-BASED DEPLOYMENT AND MONITORING OF MACHINE LEARNING MODELS IN PROVIDER NETWORKS

Techniques for dynamic accuracy-based experimentation and deployment of machine learning (ML) models are described. Inference traffic flowing to ML models and the accuracy of the models is analyzed and used to ensure that better performing models are executed more often via model selection. A predic...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: SIVASUBRAMANIAN, Swaminathan, LIBERTY, Edo, WILEY, Craig, GOODHART, Taylor, SMOLA, Alexander Johannes, KARNIN, Zohar, FAULHABER, JR., Thomas Albert, STEFANI, Stefano, LOEPPKY, Steven Andrew
Format: Patent
Sprache:eng
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Techniques for dynamic accuracy-based experimentation and deployment of machine learning (ML) models are described. Inference traffic flowing to ML models and the accuracy of the models is analyzed and used to ensure that better performing models are executed more often via model selection. A predictive component can evaluate which model is more likely to be accurate for certain input data elements. Ensemble techniques can combine inference results of multiple ML models to aim to achieve a better overall result than any individual model could on its own.