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MLOps & Data Quality · section 8 of 8

MLOps foundations

5 interview questions on mlops foundations, each answered in full. Free to read, no account needed.

What does CI/CD look like for ML?

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  • CI: on code and data changes, run unit tests, data validation (schema, ranges), training smoke tests, and model quality tests (evaluate on frozen validation set, check for regression).
  • CD: package model artifact, register in the model registry, deploy to staging, run integration and load tests, then canary/shadow to production.
  • Include DVC or MLflow for versioning of data and experiments.
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How do you make ML experiments reproducible?

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  • Pin seeds and library versions, use deterministic ops when available, containerize the environment (Docker), version code (git), data (DVC/lakeFS), and models (MLflow / model registry).
  • Log all hyperparameters and metrics per run.
  • Store the exact commit + data snapshot that produced any model in production.
  • Cache intermediate artifacts.
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What is a model card?

easy
  • Documentation artifact (Mitchell et al. 2019) accompanying a model: (1) intended use + out-of-scope uses.
  • (2) training data description + biases.
  • (3) evaluation results per subgroup.
  • (4) ethical considerations + risks.
  • (5) limitations.
  • (6) contact / owner.
  • Standard for responsible AI reporting.
  • Used by Google, HuggingFace, Microsoft.
  • Similar: Data Sheets for datasets (Gebru et al.), Model Facts labels (medical AI).
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Google's MLOps maturity levels (0-2) — what are they?

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  • Level 0: manual ML process — data scientist trains + hands off to eng for deploy.
  • Slow, error-prone, no automation.
  • Level 1: automated ML pipeline — orchestrator retrains automatically, monitors, but manual model deployment.
  • Level 2: CI/CD for ML pipelines — code changes trigger pipeline builds + tests + deploys; new models continuously deployed to production.
  • Most organizations: Level 0 or 1; Level 2 rare + hard.
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How do you organize ML teams (embedded vs central)?

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  • Central ML team: shared expertise, owns platform + tooling; downside: bottleneck + disconnected from product.
  • Embedded: ML engineers in each product team; direct problem focus; downside: duplication + platform fragmentation.
  • Hybrid (best practice): central platform team (feature store, serving infra, monitoring) + embedded ML engineers per product using platform.
  • Similar to DevOps → SRE evolution.
  • Ownership: 'you build it, you run it' — model owners on-call.
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Practise MLOps & Data Quality

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