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MLOps and LLMOps: Running Machine Learning and AI Features Reliably in Production
Building a model is a fraction of the work; keeping it accurate, reliable and affordable in production is the rest. This guide explains MLOps and its generative-AI counterpart, LLMOps, with the pipelines, monitoring and governance practices that make AI features dependable.
TopicsAI and Machine LearningArchitecture and ModernizationGenerative AI and LLMs
Key takeaways
- MLOps applies DevOps principles to machine learning, adding data versioning, experiment tracking, model registries, retraining and model monitoring.
- ML systems can fail silently: data drift and concept drift degrade accuracy without errors, so monitoring must cover data and prediction quality.
- LLMOps adapts these practices for generative AI: prompt and model versioning, evaluation sets, guardrails, cost tracking and feedback loops.
- Start simple — reproducible training, a model registry, automated deployment and basic monitoring — before adopting complex platforms.
- Treat evaluation sets and production feedback as core assets; they make every model change safer.
On this page
Many machine learning projects stall between a successful prototype and a dependable product. The model worked in a notebook; in production, data changes, performance degrades, nobody knows which version is running and retraining is a manual ordeal. MLOps is the discipline that closes that gap.
MLOps versus DevOps
| Aspect | DevOps | MLOps |
|---|---|---|
| — | — | — |
| What changes | Code and configuration | Code, data and models |
| Versioning | Source code and artefacts | Plus datasets, features, models and experiments |
| Testing | Unit, integration, end-to-end | Plus data validation, model quality, fairness and robustness |
| Deployment | Applications and services | Models as services, batch jobs or embedded components |
| Monitoring | Availability, latency, errors | Plus data drift, prediction quality and business outcomes |
| Change triggers | New code | New code, new data or degraded performance |
MLOps builds on DevOps rather than replacing it.
The ML lifecycle
- Data: ingestion, validation, labelling and versioning.
- Experimentation: tracked experiments with parameters, data versions and metrics.
- Training pipeline: reproducible, automated training.
- Evaluation: quality, fairness and robustness tests against agreed thresholds.
- Registry: approved model versions with lineage and documentation.
- Deployment: automated release as an API, batch job or embedded model, with canary or shadow testing.
- Monitoring: data drift, prediction distributions, accuracy against outcomes, latency and cost.
- Retraining: scheduled or triggered by drift, with the same evaluation gates.
Why models fail silently
- Data drift: input data changes — new customer segments, new product lines, seasonal shifts.
- Concept drift: the relationship between inputs and outcomes changes — fraud patterns, prices, behaviour.
- Upstream changes: a field renamed or a unit changed in a source system.
- Feedback loops: the model influences the data it later learns from.
Monitoring must watch inputs and outputs, and compare predictions with actual outcomes when they become available.
LLMOps: operating generative AI
| Practice | Generative AI specifics |
|---|---|
| — | — |
| Versioning | Prompts, model versions, retrieval configuration and tools |
| Evaluation | Fixed evaluation sets; rubric or model-graded scoring; human review of samples |
| Guardrails | Input and output checks, prompt-injection defences, policy filters |
| Retrieval operations | Index freshness, permissions, chunking changes |
| Cost and latency | Tokens per task, cost per successful outcome, caching |
| Feedback | User ratings, corrections and escalation outcomes |
| Provider changes | Model deprecations and updates tested before adoption |
Most generative AI incidents come from untested changes to prompts, models or retrieval.
A pragmatic maturity model
| Level | Characteristics |
|---|---|
| — | — |
| 0 — Manual | Notebooks, manual deployment, no monitoring |
| 1 — Repeatable | Scripted training, model registry, automated deployment, basic monitoring |
| 2 — Automated | Pipelines for training and evaluation, drift monitoring, scheduled retraining |
| 3 — Continuous | Triggered retraining, automated gates, A/B and shadow testing, full lineage |
Many products need level 1 or 2; level 3 suits organisations with many models.
Tools, briefly
Teams combine experiment tracking, a model registry, workflow orchestration, feature stores where many models share features, serving infrastructure and observability. Cloud platforms offer integrated suites; open-source tools allow flexibility. Choose the minimum set that covers your current maturity level.
Governance
- Document each model’s purpose, data, limitations and owner.
- Record which model version produced which decisions.
- Review fairness and risk for high-impact models.
- Control access to training data and models.
- Define who approves deployment and retraining.
Frequently asked questions
Do small teams need MLOps?
They need the basics: reproducible training, versioned models, automated deployment and monitoring. Full platforms can wait.
How often should models be retrained?
When monitoring shows degradation or when data changes materially, rather than on an arbitrary schedule alone.
Is LLMOps different from MLOps?
It shares principles but focuses on prompts, retrieval, evaluation of generated content, guardrails and token costs rather than training.
What is the most important first step?
A fixed evaluation set and production monitoring. Without them, every change is a guess.
Sources and methodology
Facts in this article were checked against the sources below on 6 November 2026. Directory data (ratings, review counts, rate bands and team sizes) changes over time and is shown as published on the date accessed. Illustrative examples are labelled as such and are not quotes.
- OWASP: Top 10 for LLM Applications 2025 — accessed 6 November 2026. Risks addressed by LLMOps guardrails
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