Scalable Architecture for Automating Machine Learning Model Monitoring

Scalable Architecture for Automating Machine Learning Model Monitoring

Last years, due to the advent of more sophisticated tools for exploratory data analysis, data management, Machine Learning (ML) model training and model serving into production, the concept of MLOps has gained more popularity. As an effort to bring DevOps processes to the ML lifecycle, MLOps aims at...

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Bibliographic Details
Main Author: de la Rúa Martínez, Javier
Format: Others
Language:English
Published: KTH, Skolan för elektroteknik och datavetenskap (EECS) 2020
Subjects:
Model Monitoring
Streaming
Scalability
Cloud-native
Data Drift
Outliers
Machine Learning
Modellövervakning
Streaming-metod
Skalbarhet
Molnbaserad
Dataskift
Outlierupptäckt
Maskininlärning
Computer and Information Sciences
Data- och informationsvetenskap
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280345

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http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280345

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