After weeks of work, a data scientist builds a model that performs well. It's shown in a presentation, everyone likes it… and months later it still isn't in production. Or it does go live, but a few months later its performance quietly drops and nobody notices.
These are two of the most common problems in AI projects. MLOps emerged precisely to solve them.
What is MLOps?
MLOps (Machine Learning Operations) is a set of practices that automate and standardise how machine learning models are built, deployed, monitored and updated. Think of it as the DevOps approach from software, adapted for machine learning.
But machine learning differs from classic software in one important way: a model's behaviour depends not only on code but also on data. Even if the code doesn't change, the model's performance can change when the data does. That's why MLOps manages data and models as well as code.
Why don't models reach production?
- Development and production differ: code that runs on a data scientist's laptop doesn't run on enterprise systems.
- No reproducibility: a model whose data, parameters and code version are unknown can't be safely deployed.
- Manual processes: when every model update is done by hand, it's slow and error-prone.
- Unclear ownership: nobody is defined as responsible for running and monitoring the model in production.
The core components of MLOps
| Component | What it does |
|---|---|
| Versioning | Tracks versions of code, data and models |
| Experiment tracking | Records the parameters and results of every training run |
| Model registry | Centrally manages approved models, their versions and status |
| CI/CD pipelines | Automate training, testing and deployment |
| Model serving | Makes the model available as an API or batch scoring job |
| Monitoring | Tracks model performance, data drift and system health |
| Retraining | Triggers model updates when performance drops |
Maturity levels
An organisation's MLOps journey usually moves through three levels:
- Level 0 – Manual: models are trained and deployed by hand. Updates are rare and painful.
- Level 1 – Automated training pipeline: training is automated, and models can be retrained regularly on new data.
- Level 2 – Full CI/CD: changes to code, data and models are automatically tested, validated and deployed.
Not every organisation needs Level 2. The right level depends on the number of models and how often they change.
LLMOps: MLOps for generative AI
LLM-based applications bring new needs:
- Prompt versioning: prompts should be versioned and tested like code
- Tracing: seeing which steps a request went through and which documents were used
- Evaluation: measuring output quality automatically, for example with LLM-as-a-judge methods where another model acts as the evaluator
- Cost and safety tracking: token usage, sensitive data leakage and misuse attempts
Widely used MLOps tools such as MLflow have added GenAI tracing and evaluation features in response.
MLOps is not AIOps
The two terms are often confused:
- MLOps manages the lifecycle of AI models.
- AIOps means using AI in IT operations, for example detecting anomalies in logs or predicting outages.
In short, MLOps is "operations for AI", while AIOps is "AI for operations".
Where to start
- Assess the current state: how many models are there, how are they deployed and monitored?
- Fix the biggest pain first: this usually starts with experiment tracking and a model registry.
- Prioritise monitoring: stopping production models from silently degrading delivers value fastest.
- Automate gradually: don't try to automate everything at once.
- Train the team: MLOps is as much about working habits as tools.
Conclusion
Building a model is the start of the AI journey; value appears when the model runs reliably in production. MLOps makes that transition systematic and sustainable.
Through our MLOps & LLMOps service we build a platform that matches your maturity level. To build this capability in your teams, see our MLOps: Taking Models to Production programme.
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