Shipping a machine learning model is not the same as shipping code. A model depends on data, drifts over time, and can degrade silently while every server stays green. MLOps is the set of practices that makes models as dependable in production as any other software component.
Why models are different
Traditional software is deterministic: the same input produces the same output. A model is a statistical artifact trained on data — and that data changes. Three new sources of risk appear.
Data dependency
The model is only as good as the data it was trained on. When the real world shifts, the model’s assumptions quietly stop holding.
Silent degradation
A bug usually announces itself with an error. A drifting model keeps returning confident answers that are simply wrong, which makes monitoring essential.
The practices that matter
MLOps borrows the DevOps mindset and adds a few model-specific disciplines.
- Versioning — track data, code and model weights together, so any prediction can be traced back to its lineage.
- Continuous training — retrain on fresh data through an automated pipeline rather than by hand.
- Monitoring for drift — watch input distributions and prediction quality, not just latency and errors.
Start with the registry
A model registry is the connective tissue between experimentation and production. It stores versions, stages and metadata, and gives you a controlled path to promote a model — or roll it back.
Conclusion
MLOps is DevOps that takes data seriously. If you already practise continuous delivery, you are halfway there; the rest is learning to treat the model, and the data behind it, as first-class citizens of your pipeline.