StreamMLOps: Operationalizing Online Learning for Big Data Streaming & Real-Time Applications-Reference-Cited by-同舟云学术

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StreamMLOps: Operationalizing Online Learning for Big Data Streaming & Real-Time Applications

Published:2023-04 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 39th International Conference on Data Engineering (ICDE)
language:
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Author:

Barry Mariam¹,Montiel Jacob²,Bifet Albert³,Wadkar Sameer⁴,Manchev Nikolay⁴,Halford Max⁵,Chiky Raja⁶,Jaouhari Saad EL⁶,Shakman Katherine B⁷,Fehaily Joudi Al⁷,Le Deit Fabrice⁸,Tran Vinh-Thuy⁸,Guerizec Eric⁸

Affiliation:

1. BNP Paribas,Institut Polytechnique de Paris,France

2. University of Waikato Amazon,United States of America

3. University of Waikato,LTCI, Télécom Paris, IP Paris,New Zealand

4. Domino Data Lab,USA & United Kingdom

5. River Maintainer Carbonfact,France

6. ISEP* - 3DS OUTSCALE,France

7. Lyra & BNP Paribas,USA & France

8. BNP Paribas

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10184508/10184509/10184778.pdf?arnumber=10184778

Reference60 articles.

1. Challenges of Machine Learning for Data Streams in the Banking Industry

2. Automating deployment containerized applications;kubernetes,2021

3. An online machine learning framework for early detection of product failures in an Industry 4.0 context

4. Domino data science platform,0

5. MStream: Fast Anomaly Detection in Multi-Aspect Streams

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