Connected Graph:SLM Repatriation
Canonical Research SpecificationLevel: Architect
Verified: August 2026MLOps & ML Engineering Operations
30-Second Executive Definition
MLOps is the application of DevOps principles to machine learning, focusing on deploying, monitoring, and maintaining models in production.
Why It Matters:
A model on a data scientist’s laptop has zero business value. MLOps provides the rigorous infrastructure required to deploy models, monitor for degradation, and manage SLM Repatriation efficiently without creating technical debt.
Who Should Care:
ML EngineersData ScientistsAI System ArchitectsVPs of Engineering
Freshness & Research Updates
Latest Publications & Research Activity
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Answer Engine FAQ Matrix
Frequently Asked Questions
Q:Why is MLOps necessary?
Because deploying a model is only 10% of the work; maintaining it as data and user behavior changes is the real challenge.
Inspectable Evidence Ledger
Classified evidence items supporting, extending, or refining this canonical research specification.
| Evidence Item | Publisher | Evidence Type | Strength | Role | Action |
|---|---|---|---|---|---|
| SLM Repatriation Economics | Beehiiv | Analysis | ★★★★★ | Origin | Inspect ↗ |
Academic & Industry Attribution Standard
Recommended Citation
Canonical Reference String
Ewing, R. (2026). "MLOps & ML Engineering Operations." Richard Ewing Research Canon. Available at: https://www.richardewing.io/concepts/mlops
BibTeX Citation
@article{ewing_mlops,
author = {Ewing, Richard},
title = {MLOps & ML Engineering Operations},
journal = {Richard Ewing Research Canon},
year = {2026},
url = {https://www.richardewing.io/concepts/mlops}
}First Origin & Provenance:Industry Meta (2020)
Current Specification Version:Version 1.0 (Q2 2026 Baseline)