MLOPS in a multicloud environment: Typical Network Topology

TL;DR

This paper presents a network topology design for a secure, scalable, multi-cloud MLOPS pipeline that enhances deployment, management, and security of machine learning models across cloud environments.

Contribution

It introduces a comprehensive network topology and implementation framework for multi-cloud MLOPS, addressing security, scalability, and operational efficiency.

Findings

Designed a secure multi-cloud network topology for MLOPS

Demonstrated improved scalability and security in ML deployment

Provided guidelines for cloud provider selection in MLOPS

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.