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Review Article Open Access December 27, 2023

MLOps Frameworks for Reliable Model Deployment in Cloud Data Platforms

Velangani Divya Vardhan Kumar Bandi 1,*
1
Sr Data Engineer, USA
Publihed in: Current Research in Public Health (Volume 3, Issue 1, 2023)
Page(s): 84-101
DOI: 10.31586/jaibd.2023.1368
Received
September 21, 2023
Revised
October 31, 2023
Accepted
December 21, 2023
Published
December 27, 2023
Keywords
MLOps; Cloud-Native Machine Learning; Continuous Integration for ML; Continuous Delivery of Models; ML Reliability Engineering; Model Deployment Pipelines; Data Lineage and Reproducibility; ML Governance and Security; Service-Level Objectives (SLOs); Monitoring and Incident Response Automation; CI/CD for Machine Learning; Model Rollout Strategies; Containerization for ML (Docker); Orchestration for ML Workloads (Kubernetes); Model Serving Frameworks; High-Throughput Low-Latency Inference; Auto-Scaling ML Services; Model Versioning; Cloud Data Platform Architecture; Production ML Observability
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright: Copyright © The Author(s), 2023. Published by Scientific Publications
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APA Style
Bandi, V. D. V. K. (2023). MLOps Frameworks for Reliable Model Deployment in Cloud Data Platforms. Current Research in Public Health, 3(1), 84-101. https://doi.org/10.31586/jaibd.2023.1368
ACS Style
Bandi, V. D. V. K. MLOps Frameworks for Reliable Model Deployment in Cloud Data Platforms. Current Research in Public Health 2023 3(1), 84-101. https://doi.org/10.31586/jaibd.2023.1368
Chicago/Turabian Style
Bandi, Velangani Divya Vardhan Kumar. 2023. "MLOps Frameworks for Reliable Model Deployment in Cloud Data Platforms". Current Research in Public Health 3, no. 1: 84-101. https://doi.org/10.31586/jaibd.2023.1368
AMA Style
Bandi VDVK. MLOps Frameworks for Reliable Model Deployment in Cloud Data Platforms. Current Research in Public Health. 2023; 3(1):84-101. https://doi.org/10.31586/jaibd.2023.1368 
@Article{crph1368,
AUTHOR = {Bandi, Velangani Divya Vardhan Kumar},
TITLE = {MLOps Frameworks for Reliable Model Deployment in Cloud Data Platforms},
JOURNAL = {Current Research in Public Health},
VOLUME = {3},
YEAR = {2023},
NUMBER = {1},
PAGES = {84-101},
URL = {https://www.scipublications.com/journal/index.php/JAIBD/article/view/1368},
ISSN = {2831-5162},
DOI = {10.31586/jaibd.2023.1368},
ABSTRACT = {Machine learning operations (MLOps) comprises the practices, methods, and tooling that facilitate the deployment of reliable ML models in production environments. While many aspects of cloud data platforms are designed to enable reliability, only some managed ML services support the MLOps goals of continuous integration, continuous delivery, data lineage tracking, associated reproducibility, governance, and security. Furthermore, reliability encompasses not only the fulfillment of service-level objectives, but also systematic monitoring, alerting, and incident response automation. Architectural patterns are proposed to enable reliable deployment in cloud data platforms, focusing on the implementation of continuous integration and testing pipelines for ML models and the formulation of continuous delivery and rollout strategies. Continuous integration pipelines reduce the risk of regressions and ensure sufficient model performance at the time of deployment, while continuous delivery pipelines enable rapid updates to production models within acceptable risk profiles. The landscape of publicly available MLOps frameworks, tools, and services is also examined, emphasizing the pros and cons of established and rising solutions in containerization, orchestration, model serving, and inference. Containerization and orchestration contributes to the building of reliable deployment pipelines in cloud data platforms, whether general-purpose tools (e.g. Docker and Kubernetes) or solutions tailored for ML workloads. Containerized serving frameworks designed for high-throughput, low-latency inference can benefit a wide range of business applications, while auto-scaling and model versioning capabilities enhance the ease of use of cloud-native ML services.},
}
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%D 2023
%J Current Research in Public Health

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%U https://www.scipublications.com/journal/index.php/JAIBD/article/view/1368

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AB  - Machine learning operations (MLOps) comprises the practices, methods, and tooling that facilitate the deployment of reliable ML models in production environments. While many aspects of cloud data platforms are designed to enable reliability, only some managed ML services support the MLOps goals of continuous integration, continuous delivery, data lineage tracking, associated reproducibility, governance, and security. Furthermore, reliability encompasses not only the fulfillment of service-level objectives, but also systematic monitoring, alerting, and incident response automation. Architectural patterns are proposed to enable reliable deployment in cloud data platforms, focusing on the implementation of continuous integration and testing pipelines for ML models and the formulation of continuous delivery and rollout strategies. Continuous integration pipelines reduce the risk of regressions and ensure sufficient model performance at the time of deployment, while continuous delivery pipelines enable rapid updates to production models within acceptable risk profiles. The landscape of publicly available MLOps frameworks, tools, and services is also examined, emphasizing the pros and cons of established and rising solutions in containerization, orchestration, model serving, and inference. Containerization and orchestration contributes to the building of reliable deployment pipelines in cloud data platforms, whether general-purpose tools (e.g. Docker and Kubernetes) or solutions tailored for ML workloads. Containerized serving frameworks designed for high-throughput, low-latency inference can benefit a wide range of business applications, while auto-scaling and model versioning capabilities enhance the ease of use of cloud-native ML services.
DO  - MLOps Frameworks for Reliable Model Deployment in Cloud Data Platforms
TI  - 10.31586/jaibd.2023.1368
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