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

arXiv: https://arxiv.org/abs/2503.15577 · HTML: https://mlsanity.com/paper/2503.15577/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2025.

## Built on by (most cited first, 3 of 3)

- [Towards Secure MLOps: Surveying Attacks, Mitigation Strategies, and Research Challenges](https://mlsanity.com/paper/2506.02032/) (2025)
- [Optimal Resource Allocation for ML Model Training and Deployment under Concept Drift](https://mlsanity.com/paper/2512.12816/) (2025)
- [Quality Model for Machine Learning Components](https://mlsanity.com/paper/2602.05043/) (2026)

## Abstract (arXiv)

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.