Machine Learning Engineer
hardmachine-learning-engineer-model-versioning
How do you version and promote models safely (dev → staging → prod)?
Answer
Treat models like deployable artifacts.
Use:
- Model registry with semantic versioning
- Automated eval gates (quality, bias, latency)
- Canary/shadow deployments
- Rollback to known-good versions
Always attach metadata: training data hash, code version, feature set, and evaluation results for auditability.
Related Topics
MLOpsReleaseGovernance