Design ML deployment with GitHub and Jenkins

Q: Design ML deployment with GitHub and Jenkins

This is a Machine Learning interview question from Capital One for Data Scientist roles. View the full question and solution on PracHub.

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Question

Design an end‑to‑end ML deployment for a prediction model using GitHub and Jenkins: 1) Propose a repo layout (src/, features/, data_contracts/, tests/, notebooks/, ci/, docker/) and how you pin environments (lockfiles, Docker, CUDA matrix). 2) Explain model/data versioning (semantic model versions, commit SHAs, DVC/LakeFS, artifact store), model registry, and provenance capture so any prediction is reproducible. 3) Define CI checks (unit tests, data‑schema tests, leakage guards, determinism checks, small smoke‑train) and CD gates (minimum offline metrics, bias metrics, size/latency budgets). 4) Describe Jenkins pipeline stages (build, test, train, evaluate, package, sign, deploy) and release strategies (blue/green, canary, shadow, rollback with feature flags). 5) Detail runtime monitoring (data drift, prediction drift, performance, latency, error budgets, 24/7 alerts) and automated retraining triggers and approval workflows. 6) Cover secrets/PII handling, access control, and how you’d run an incident response and rollback within 15 minutes.

Design ML deployment with GitHub and Jenkins

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