Explain ML pipelines and typical CI/CD/CT components
Tests if you separate code CI/CD from model CT and grasp ML automation. Cover source control, build, tests, deploy for code; data validation, training, evaluation, promotion for CT. Red flag: treating ML like software CI/CD and ignoring data or registry gates.
Tests if you distinguish software CI/CD from Continuous Training and understand data, code, and model flows in ML automation. A strong answer lists source control, testing, builds, and deployment for code; then data validation, reproducible training, offline evaluation, registry gates, and canary release for CT. It also covers monitoring and feedback loops that trigger retraining. Red flag: treating ML like software CI/CD while omitting data lineage, model registries, or rollback strategies.
Read the original → docs.cloud.google.com
- #mlops
- #ci/cd
- #machine-learning
- #pipeline
- #infrastructure
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