How would you build CI/CD for an ML model?
Source: interviewintermediate
WHAT IT TESTS: MLOps maturity beyond app deployment. OUTLINE: data and model versioning, automated training plus evaluation gates, model registry, deployment with monitoring and retraining triggers.
WHAT IT TESTS: whether you grasp MLOps versus traditional CI/CD. ANSWER OUTLINE: version data and code together; continuous training that retrains on new data; evaluation gates comparing the candidate against the production baseline; a model registry; deployment via canary or shadow; and monitoring that watches drift to trigger retraining. The extra dimensions are data and model artifacts, not just code.
Read the original → interview
- #mlops
- #ci-cd
- #model-deployment
- #cloud
- #machine-learning
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