Open Access
American Research Journal of Computer Science and Information Technology
ISSN (Online): 2572-2921
DOI: 10.46568/arjcsit
MLOps and Continuous ML Delivery Pipelines
Abstract
Surging rates of machine learning deployment in real-world applications have highlighted the need for improved systems
built to support large-scale, dependable and trustworthy AI solutions. Extending DevOps practices, MLOps focuses on
ML lifecycle management to address the demands of handling data versions, re-training models, managing governance
and delivering ML services continually. This article thoroughly examines how to use MLOps to deploy, monitor and
develop machine learning models in many different work settings. We observe through history how MLOps progressed
from managing models manually to the present use of automated and professional systems. key elements such as data
engineering, model training using automation, validation workflow, the process to deploy the model and post-deployment
monitoring are carefully looked at. This domain specializes in issues that distinguish ML systems from traditional software
systems. It means a change in the inputs, change in the data and the need for frequent updates to the model. We discuss
how to manage model governance effectively, paying attention to checking versions and ethical approval, as well as
the importance of automating infrastructure for complex ML tasks. It also focuses on future changes in MLOps such as
adopting foundation models, using AutoML for better pipeline design and focusing on edge deployment strategies. It
brings together industrial processes, academic findings and case study reviews to guide researchers and practitioners
in making machine learning useful for many users. The findings underscore MLOps’ pivotal role in enabling sustainable,
trustworthy, and agile AI systems across industries.
