The adoption of Machine Learning Operations (MLOps) enables automation and reliable model deployments across industries. However, differing MLOps lifecycle frameworks and maturity models proposed by industry, academia, and organizations have led to confusion regarding standard adoption practices. This paper introduces a unified MLOps lifecycle framework, further incorporating Large Language Model Operations (LLMOps), to address this gap. Additionally, we outlines key roles, tools, and costs associated with MLOps adoption at various maturity levels. By providing a standardized framework, we aim to help organizations clearly define and allocate the resources needed to implement MLOps effectively.
@article{arxiv.2503.15577,
title = {Navigating MLOps: Insights into Maturity, Lifecycle, Tools, and Careers},
author = {Jasper Stone and Raj Patel and Farbod Ghiasi and Sudip Mittal and Shahram Rahimi},
journal= {arXiv preprint arXiv:2503.15577},
year = {2025}
}