MLOps connects data preparation, training, evaluation, release, and monitoring so a model can be reproduced and corrected after deployment. Code version alone does not identify a model artifact.
Choose a starting point
Trace dataset and feature versions through a training run. Then define promotion checks, serving behavior, drift monitoring, rollback, and incident ownership. Test the full path with the projects.
- MLOps Tutorial
- Training manifests: link data, code, configuration and artifact
- Shadow and canary rollout: compare a candidate without losing a rollback
- Feature contracts: admit only usable inference records
- Inference latency budgets: measure queue, feature and model time
- Model artifacts: verify digest, origin and loading format
- Prediction-outcome joins: evaluate only mature, matched decisions
- Pipeline stages: cache by complete input identity
- Batch inference: make partitions idempotent and outputs identifiable
Common Mistakes
A healthy endpoint can serve an outdated model or an incompatible feature shape. The linked lessons show how to detect those failures before trusting a production prediction.