MLOps8 min read
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MLOps: From Notebook to Production
The steps between a working notebook and a reliable production model — and how to automate them.
A notebook that produces a good model is the start of the work, not the end. MLOps is the set of practices that make model training, deployment and monitoring repeatable and safe.
The gap between notebook and production
- Data access and features that differ between training and serving
- Training runs that cannot be reproduced
- Manual handoffs to deploy a model
- No monitoring once the model is live
Building the path
- Version data, code and configuration together; track experiments with a tool such as MLflow
- Turn training into a pipeline (for example Kubeflow Pipelines, Airflow or Argo Workflows)
- Register models with metadata, lineage and approval states
- Deploy through CI/CD with staged rollouts such as shadow or canary
- Monitor latency, errors, data drift and prediction quality
Close the loop
Monitoring should feed back into retraining decisions. Define what triggers a retrain — drift thresholds, schedule or new labeled data — and automate the steps that are safe to automate.
Start small
Automate one model end to end before building a general platform. The lessons from that first path shape what the platform should standardize.