What is MLOps?
MLOps (machine learning operations) is the set of practices and tools that take machine learning models from experiment to production and keep them accurate, reliable, secure and cost-effective over time. It combines machine learning, DevOps and data engineering.
Models degrade after launch because the world changes: customer behaviour shifts, products change and data sources evolve. Without MLOps, teams discover problems late, cannot reproduce results and depend on manual steps known to one person. With it, deployment, monitoring and retraining become routine.
Key takeaways
- Version data, code, models and prompts together so every result is reproducible.
- Automate testing and deployment, including evaluation against a fixed benchmark.
- Monitor data drift, model quality, cost and latency – not just uptime.
- Define retraining triggers in advance and automate the loop.
MLOps by the numbers
- On average 48% of AI projects make it into production, and it takes about 8 months to go from prototype to production (Gartner survey of 644 organisations, May 2024).
- 45% of leaders at organisations with high AI maturity say their AI initiatives stay in production for three years or more, against 20% at low-maturity organisations (Gartner, June 2025).
- Data availability and quality remain a top barrier: cited by 34% of low-maturity and 29% of high-maturity leaders (Gartner).
Seven MLOps best practices
1. Version everything together
Track datasets, feature definitions, training code, hyperparameters, model artefacts and – for LLM applications – prompts and retrieval configuration. A model registry links each deployed version to exactly what produced it.
2. Automate the pipeline with CI/CD
Every change triggers automated data validation, unit tests and model evaluation against a benchmark set. Only versions that meet defined thresholds are promoted.
3. Deploy gradually
Use shadow deployments, canary releases or A/B tests so a new model serves a small share of traffic before full rollout, with one-step rollback.
4. Monitor what matters
| Signal | What to watch |
|---|---|
| Data drift | Input distributions moving away from training data |
| Model quality | Accuracy, precision/recall, or answer quality and groundedness for LLMs |
| Operations | Latency, throughput, error rates, cost per prediction or request |
| Responsible AI | Bias indicators, user feedback, incident reports |
"A model in production is a living system. Without monitoring and retraining, its accuracy quietly decays – and so does the trust of the people who rely on it."
5. Plan retraining
Define triggers – a schedule, a drift threshold or a volume of new labelled data – and automate retraining, evaluation and promotion. Where real data is scarce or sensitive, privacy-preserving synthetic data can fill gaps.
6. Secure the ML supply chain
Control who can change data, models and pipelines; scan dependencies; protect model endpoints; and keep audit trails for compliance with regulations such as GDPR.
7. Manage cost deliberately
Right-size models and infrastructure, cache frequent results, batch where real-time is not needed, and track cost per prediction alongside accuracy.
MLOps vs DevOps vs LLMOps
| DevOps | MLOps | LLMOps | |
|---|---|---|---|
| What changes | Code | Code, data and models | Prompts, retrieval data, models and guardrails |
| Main failure mode | Bugs | Data and concept drift | Wrong or ungrounded answers, prompt injection |
| Key tests | Unit and integration tests | Data validation and model evaluation | Evaluation sets for answer quality and safety |
MLOps maturity levels
- Manual: notebooks, manual deployment, no monitoring.
- Repeatable: versioned code and models, scripted deployment, basic monitoring.
- Automated: CI/CD for models, drift and quality monitoring, triggered retraining.
- Governed: full lineage, approval workflows, cost and responsible-AI metrics, audit-ready.
Most organisations benefit most from moving from level 1 to level 2 or 3 for their highest-value models first.
Frequently Asked Questions
Why do machine learning models degrade in production?
Because the data they see changes over time (data drift) or the relationship between inputs and outcomes changes (concept drift). Monitoring detects this; retraining corrects it.
What tools are used for MLOps?
Typical components are a data versioning tool, an experiment tracker and model registry, a CI/CD system, a model serving platform and monitoring. Managed services on AWS, Azure and Google Cloud provide many of these.
Do LLM applications need MLOps?
Yes. LLM applications need versioned prompts and retrieval data, evaluation sets, guardrails and monitoring of answer quality, cost and safety – often called LLMOps.
How often should a model be retrained?
There is no universal interval. Retrain when monitoring shows drift or declining quality, when significant new labelled data is available, or on a schedule matched to how fast your domain changes.
What is a model registry?
A central catalogue of model versions with their metadata, lineage, evaluation results and deployment status, used to control which version runs in production.
How Assystant can help
Assystant’s Data & MLOps Services cover data strategy and AI readiness, MLOps managed services, model monitoring and retraining, database optimisation and synthetic data generation for privacy-sensitive domains. Learn more or book a free consultation.