What is MLOps?
MLOps, short for machine learning operations, is the set of practices and tooling for moving models from a data scientist’s experiments into production and keeping them working there. It borrows the ideas of DevOps (version control, automated testing, continuous delivery, monitoring) and extends them to cover the two things that make machine learning different: the data and the trained model are artifacts that change independently of the code.
In practice MLOps covers experiment tracking, machine learning pipelines for repeatable training, a model registry with versions and approvals, model deployment for batch or real-time inference, model monitoring for drift and accuracy, and a retraining loop. It also covers the governance around those steps: who may promote a model, what evidence is required, and how a decision can be traced back to the model version that made it.
How MLOps works
A mature setup runs as a loop. Training code and data references live in version control. A pipeline trains a candidate model on a schedule or when new data arrives, evaluates it against the current production model, and registers it. A person reviews the evaluation report and approves promotion. The deployment system pulls the approved version from the registry and serves it. Monitoring compares live input distributions and prediction outcomes with the training baseline. When drift or falling accuracy crosses a threshold, the loop triggers retraining or a human review, and every step leaves a record.
Teams rarely start there. A common progression is manual training with a tracked registry first, then automated retraining pipelines, then automated evaluation gates with continuous delivery of approved models. Each stage is useful on its own.
MLOps compared with DevOps
| Aspect | DevOps | MLOps |
|---|---|---|
| What is versioned | Code and configuration | Code, data snapshots, features, and trained models |
| What testing checks | Unit and integration tests pass | Data validation passes and the model beats a baseline on holdout data |
| What causes failure | A code change or an outage | A change in the data or in the world, with no code change at all |
| What monitoring watches | Uptime, errors, and latency | Those plus input drift, prediction drift, and outcome accuracy |
| How rollback works | Redeploy the previous build | Redeploy the previous model together with its feature definitions |
MLOps in practice
A healthcare provider network runs a model that predicts which appointments are likely to be no-shows so clinics can overbook sensibly. Scoring runs nightly as a batch job. A weekly report shows input drift and the hit rate of last week’s predictions once attendance is known. Retraining runs quarterly through a pipeline, and the clinical operations director approves each new version in the registry after reading the evaluation report. When a new scheduling module changed the appointment type codes, the pipeline’s validation gate stopped the run before the model scored a week of unrecognizable inputs. Without that gate, the clinics would have received confident predictions based on garbage.
MLOps and private AI
The same discipline applies to language models and agents running on infrastructure a company controls. The model may be downloaded rather than trained, but it still has a version, an evaluation record, a deployment, and behavior that must be monitored and rolled back when it degrades. Prompts, retrieval indexes, and tool definitions become additional versioned artifacts. FlatClaw, the open-source private AI platform from Kirk Tech Solutions, applies this pattern by pinning model versions, logging every inference and tool call, and requiring an approval step before an agent changes a system of record.
What to watch for
- Start with monitoring and a registry before buying automation, because knowing which model is in production and whether it still works solves most early problems.
- Assign an owner for each production model who receives drift alerts and decides on retraining, or alerts will be ignored.
- Check that features are computed identically in training and in serving, since this skew is the most common cause of a model that tested well and performs badly.
- Keep approval steps human for models that affect credit, health, employment, or safety, and record who approved what.
- Match the tooling to the number of models; two models do not justify a platform program, while twenty cannot be run by hand.