Navigating MLOps: Insights into Maturity, Lifecycle, Tools, and Careers

Fuente: arXiv
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Autores principales: Stone, Jasper, Patel, Raj, Ghiasi, Farbod, Mittal, Sudip, Rahimi, Shahram
Formato: Preprint
Publicado: 2025
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author Stone, Jasper
Patel, Raj
Ghiasi, Farbod
Mittal, Sudip
Rahimi, Shahram
author_facet Stone, Jasper
Patel, Raj
Ghiasi, Farbod
Mittal, Sudip
Rahimi, Shahram
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2503_15577
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Navigating MLOps: Insights into Maturity, Lifecycle, Tools, and Careers
Stone, Jasper
Patel, Raj
Ghiasi, Farbod
Mittal, Sudip
Rahimi, Shahram
Software Engineering
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.
title Navigating MLOps: Insights into Maturity, Lifecycle, Tools, and Careers
topic Software Engineering
url https://arxiv.org/abs/2503.15577