CI/CD for ML & Data Code
Why the artifact is model + data + code, and how to gate merges and deploys on all three
Learn what makes CI/CD for ML and data pipelines different from general software CI: unit tests for data transforms, schema and data-contract validation gates, model-quality gates that block a merge on offline metric regression, and continuous deployment of both code and models through staging to production — all built on the core mental model that a model trained on different data is a different artifact requiring the same review rigor as a code change.
Practice questions (5)
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A One-Line Training-Window Change That Isn't a One-Line Change
Intermediate · Free -
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Design a Data Contract Gate for a Feature Pipeline
Intermediate -
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Calibrate a Model-Quality Gate's Tolerance Band
Intermediate -
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Diagnose an Incident: Code Deploy or Model Promotion?
Intermediate -
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Design the CI/CD Pipeline for a New Fraud-Detection Repo
Intermediate