Authors: Penghao Liang, Bo Song, Xiaoan Zhan, Zhou Chen, Jiaqiang Yuan

Published on: May 16, 2024

Impact Score: 7.8

Arxiv code: Arxiv:2405.09819

Summary

• What is new: Focuses on integrating automated model training with traditional CI/CD pipelines using version control, versioning environments, and containerization to enhance MLOps.
• Why this is important: Challenges in deploying and monitoring machine learning models in real-world applications.
• What the research proposes: Proposes integration of automated training, enhanced version control, containerization, and continuous monitoring into MLOps frameworks to improve model reliability and performance.
• Results: Improved productivity and reliability in machine learning operations as evidenced by case studies from Netflix.

Technical Details

Technological frameworks used: MLOps, CI/CD

Models used: Automated model training systems

Data used: Case studies from Netflix

Potential Impact

Tech companies, especially those in streaming, e-commerce, and any sector relying heavily on machine learning for operational enhancements

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