# MLOps

> MLOps is the discipline of operating machine learning systems in production: versioning, deployment, monitoring, retraining, and governance.

Category: Production
Source: https://impetora.com/glossary/mlops
Part of: Impetora AI consulting glossary (https://impetora.com/glossary)

## What is MLOps?

MLOps applies software engineering and DevOps practices to ML. Core capabilities include data and model versioning, reproducible training, automated evaluation, deployment with canary or shadow patterns, drift monitoring, and rollback. Mature MLOps platforms also track lineage, feature stores, and approvals. The goal is the same as DevOps: make change safe, fast, and auditable.

## How does MLOps apply to enterprise AI?

Enterprises with multiple ML models in production need MLOps to avoid silent degradation, regulatory non-compliance, and accidental data leakage. Under the EU AI Act, MLOps tooling provides much of the post-market monitoring evidence regulators expect.

## Related terms

- [LLMOps](https://impetora.com/glossary/llmops) - LLMOps is the subset of MLOps focused on the specific operational concerns of large language models: prompt versioning, evaluation, cost control, and output observability.
- [Model Drift](https://impetora.com/glossary/model-drift) - Model drift is the gradual or sudden degradation of a model's performance in production caused by changes in input data, target distribution, or operating context.
- [Observability](https://impetora.com/glossary/observability) - Observability for AI is the ability to understand what an AI system did, why it did it, and at what cost, by inspecting its inputs, outputs, intermediate steps, and metrics.
- [AI Audit Trail](https://impetora.com/glossary/ai-audit-trail) - An AI audit trail is the persistent, tamper-evident record of every input, output, tool call, model version, and decision an AI system has made, sufficient to reconstruct any past interaction.

## External references

- [Google, MLOps continuous delivery](https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning)

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