MLOPS in a multicloud environment: Typical Network Topology
Boyang Yan
TL;DR
The paper addresses deploying secure, scalable MLOPS in a multicloud setting by designing a cloud-native pipeline that automates data collection, model lifecycle management, and real-time inference while ensuring data residency, security, and high availability. It compares major cloud providers against a structured set of business and technical requirements and selects Azure as the primary platform with CoreWeave for training, detailing the associated services and topology. The work presents two design iterations, mapping requirements to architecture pillars, and validating them through security, monitoring, and scalability considerations, complemented by Kubernetes experimentation using AKS and Istio canaries. It also discusses tradeoffs between security, performance, cost, and reliability, and outlines future directions such as richer Kubernetes deployments and deeper cross-provider comparisons. Overall, the study provides a concrete blueprint for multicloud MLOPS, balancing governance, automation, and scalability to enable rapid, secure ML lifecycle management in enterprise settings.
Abstract
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.