Maple 1: package the exact model you evaluated
Advanced MLOps · Pin artifact identity, validate inputs and separate compatibility checks from predictive quality.
Explore & practiseThe practice & career studioTurn what you know
Turn what you know
into work you can explain.
Investigate a mistake. Solve a small challenge. Build a case you can defend. Original resources that help you move from reading to doing.
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Advanced / Rehearse reliable AI releases ↗Advanced / Evaluate AI evidence and retrieval ↗Advanced / Reason about cause and effect ↗Advanced / Evaluate useful forecasts ↗Advanced / Build reliable analytics models ↗Advanced / Start the Meridian evaluation labs ↗15 min / Solve a SQL challenge ↗15 min / Diagnose a misleading total ↗45 min / Prepare an honest résumé ↗
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Supporter offline project desk ↗Advanced MLOps & AI deployment
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194 resources
Advanced MLOps & AI deployment
Release contracts, per-version gates, label-aware monitoring, rollback and incident evidence.
Maple 2: evaluate a canary without hiding version failures
Advanced deployment lab · Use per-version denominators, predeclared gates and explicit holds for insufficient evidence.
Explore & practiseMaple 3: monitor service health and model quality separately
Advanced MLOps review · Treat missing labels as unknown quality, distinguish drift from degradation and define a monitoring contract.
Explore & practiseMaple 4: rehearse rollback and write an AI deployment review
Advanced portfolio and incident exercise · Connect release evidence, owner roles and recovery limits without overstating a local simulation.
Explore & practiseResume guidance and examples are for learning and preparation only. They do not guarantee interviews, a job offer or employment.
These original companion activities are free. Advanced offline packs are separate supporter extras; companion activities do not change saved course progress.
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