AI/ML OpsArchitect

ML Production Patterns

Drift, shadow deploys, retraining, evals — the boring 70%

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Two full lessons from ML Production Patterns — exact topics, hands-on lab pairings, same depth as the paid course. Watch the videos free; sign up to access labs + the rest of the curriculum.

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Lesson 0122 min

The 5 failure modes of production ML

Production ML systems fail in predictable ways. We catalogue the top 5 — feature drift, label drift, concept drift, infrastructure drift, eval drift — with real anonymized client examples.

What this lesson teaches

· Feature drift: input distribution changes (often during external events)
· Concept drift: the relationship between features and label changes
· Label drift: ground truth definition changes (annotation policy shifts)
· Infrastructure drift: silent dependency upgrades break inference
· Eval drift: holdout set becomes stale and stops representing reality

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Lesson 0228 min

Shadow deployments + canary patterns for ML

How to safely roll out new model versions in production. Shadow deploys (run new alongside old, compare without affecting users), canary deploys (gradually shift traffic), and the rollback patterns that actually work.

What this lesson teaches

· Shadow deploy: capturing predictions without serving them
· Online comparison: bias-corrected lift estimation
· Canary deploy: 5% → 25% → 50% → 100% traffic shifts
· Rollback triggers: which metrics matter (and which to ignore)
· Multi-armed bandit alternatives to A/B testing for ML

● Paired lab

Implement shadow deploy + canary for an XGBoost classifier on real production data.

These are 2 of 40 lessons. Subscribe to @computerpathshala654 for new lessons + course launches. The full 38 remaining lessons are included with cohort enrolment, with a 7-day money-back guarantee.

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