On Continuous Integration / Continuous Delivery for Automated Deployment of Machine Learning Models using MLOps

TL;DR

This paper explores the integration of CI/CD pipelines within MLOps for automated deployment of machine learning models, highlighting tools, challenges, and deployment strategies like GitOps.

Contribution

It provides an in-depth analysis of MLOps, compares it with DevOps, and discusses CI/CD tools and deployment methods specific to machine learning workflows.

Findings

Identifies key differences between DevOps and MLOps.

Analyzes CI/CD tools suitable for machine learning.

Discusses deployment strategies including GitOps.

Abstract

Model deployment in machine learning has emerged as an intriguing field of research in recent years. It is comparable to the procedure defined for conventional software development. Continuous Integration and Continuous Delivery (CI/CD) have been shown to smooth down software advancement and speed up businesses when used in conjunction with development and operations (DevOps). Using CI/CD pipelines in an application that includes Machine Learning Operations (MLOps) components, on the other hand, has difficult difficulties, and pioneers in the area solve them by using unique tools, which is typically provided by cloud providers. This research provides a more in-depth look at the machine learning lifecycle and the key distinctions between DevOps and MLOps. In the MLOps approach, we discuss tools and approaches for executing the CI/CD pipeline of machine learning frameworks. Following that, we take a deep look into push and pull-based deployments in Github Operations (GitOps). Open exploration issues are also identified and added, which may guide future study.