Machine Learning Operations: A Mapping Study
Abhijit Chakraborty, et al.
Comprehensive synthesis of how enterprises operationalize machine learning across deployment, observability, governance, and compliance. The study distills patterns across Fortune 500 and high-regulation environments to provide practical guidance for MLOps teams.
Abstract
We survey modern MLOps tooling and processes across industries with high regulatory pressure. The mapping identifies maturity models for model deployment, CI/CD, data governance, and monitoring. We highlight organizational patterns that enable trustworthy, production-grade machine learning in complex enterprises.
Key Takeaways
- Identifies four maturity archetypes for ML deployment pipelines and explains the engineering investments required to progress between them.
- Emphasizes observability, lineage, and human-in-the-loop review as critical safeguards for compliant AI systems.
- Provides a playbook for aligning product, data science, and platform teams around SLAs and operational KPIs.