Machine Learning
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MLOps Model Lifecycle & Governance
The full data-to-retrain lifecycle, versioning discipline, approval gates, and audit-ready model cards
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Learn the complete model lifecycle as a single governed pipeline, why every stage transition needs an owner and a sign-off, how to version data/code/model/environment so any prediction is reproducible months later, and what a model card documents and why regulated teams cannot ship without one.
Practice questions (6)
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Reconstruct a Denied-Loan Decision for a Regulator
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Design a Risk-Tiered Approval Workflow
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Find the Broken Links in a Reproducibility Chain
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A Model Card That Missed Its Own Limitation
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An Automated Retrain That Skipped Approval
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