MLOPS in a multicloud environment: Typical Network Topology
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914891949932544 |
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| author | Yan, Boyang |
| author_facet | Yan, Boyang |
| contents | As artificial intelligence, machine learning, and data science continue to drive the data-centric economy, the challenges of implementing machine learning on a single machine due to extensive data and computational needs have led to the adoption of cloud computing solutions. This research paper explores the design and implementation of a secure, cloud-native machine learning operations (MLOPS) pipeline that supports multi-cloud environments. The primary objective is to create a robust infrastructure that facilitates secure data collection, real-time model inference, and efficient management of the machine learning lifecycle. By leveraging cloud providers' capabilities, the solution aims to streamline the deployment and maintenance of machine learning models, ensuring high availability, scalability, and security. This paper details the network topology, problem description, business and technical requirements, trade-offs, and the provider selection process for achieving an optimal MLOPS environment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_20494 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MLOPS in a multicloud environment: Typical Network Topology Yan, Boyang Networking and Internet Architecture As artificial intelligence, machine learning, and data science continue to drive the data-centric economy, the challenges of implementing machine learning on a single machine due to extensive data and computational needs have led to the adoption of cloud computing solutions. This research paper explores the design and implementation of a secure, cloud-native machine learning operations (MLOPS) pipeline that supports multi-cloud environments. The primary objective is to create a robust infrastructure that facilitates secure data collection, real-time model inference, and efficient management of the machine learning lifecycle. By leveraging cloud providers' capabilities, the solution aims to streamline the deployment and maintenance of machine learning models, ensuring high availability, scalability, and security. This paper details the network topology, problem description, business and technical requirements, trade-offs, and the provider selection process for achieving an optimal MLOPS environment. |
| title | MLOPS in a multicloud environment: Typical Network Topology |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2407.20494 |