MLOps for Small Teams: A Practical Playbook
How lean teams can ship reliable ML without massive infrastructure.
You do not need a huge platform to run MLOps. You need discipline around experiments, models, and monitoring.Use experiment tracking to capture parameters, metrics, and artifacts. MLflow provides a tracking UI and APIs to log runs and compare experiments.
Add a registry for model lifecycle management. MLflow Model Registry offers versioning, lineage, and stages so teams can promote models safely.
Keep data versions tied to model versions. If you cannot reproduce the dataset, you cannot reliably reproduce the model.
Finally, set up basic monitoring: data drift, performance drop, and latency. Small teams can do a lot with a simple dashboard and alerts.
Key takeaways
- Tracking and registries are the minimum MLOps stack.
- Data versioning is essential for reproducibility.
- Basic monitoring prevents silent regressions.
- Small teams can deliver reliable ML with discipline.
Checklist
- Experiment tracking in place
- Model registry with lifecycle stages
- Data versioning strategy defined
- Monitoring for drift and latency
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