Model Drift
A model that scored 94% accuracy on launch day can quietly degrade to 71% six months later - without a single line of code changing. Model drift is the silent reliability killer of production AI systems. This course teaches you to detect it statistically, diagnose its root cause, and respond with the right tool: retrain, recalibrate, or rollback. Includes a 40-point prevention checklist you can run before and after every deployment.
Course Lessons
From the theory to the statistics to the response playbook - follow in order or jump to any topic.
1. The Silent Failure
What model drift is, why it hides from conventional monitoring, and the real cost of catching it late.
2. The Drift Taxonomy
Data drift, concept drift, and prediction drift - what each one is, what triggers it, and what signals betray it.
3. Statistical Detection
PSI, KL divergence, KS test, and Jensen-Shannon - the four methods that catch drift before users do, with worked examples.
4. The Monitoring Stack
Four monitoring layers, baseline vs. sliding windows, threshold-setting, and how to integrate drift alerts into your existing observability stack.
5. LLM Drift
How LLMs drift differently from classical ML models: prompt sensitivity, knowledge staleness, embedding shift, and output format decay.
6. Root Cause Analysis
A five-step framework for diagnosing whether drift came from your data, your model, your evaluation, or the world itself.
7. Retrain, Recalibrate, or Rollback
The three-path response framework and the decision matrix that tells you which path to take - before the incident starts.
8. The Drift Prevention Checklist
40 checkpoints across pre-deploy, monitoring, alert response, and prevention hygiene - plus the five-level drift maturity model.
What You Will Learn
By the end of this course, you will be able to:
Spot Drift Early
Run PSI, KS, and embedding-distance checks that catch degradation weeks before it shows up in user complaints.
Understand LLM-Specific Drift
Know how prompt sensitivity, knowledge cutoff, and output format decay differ from classical ML drift - and how to monitor each.
Diagnose Root Cause
Distinguish data drift from concept drift from evaluation drift in five structured steps, so you pick the right fix.
Run the Playbook
Use the 40-point checklist and retrain/recalibrate/rollback decision matrix to respond to drift without firefighting.
Go Deeper: Companion Courses
Model drift is one chapter of the production reliability story. These courses cover the rest.
Production Readiness Runbook for LLM Systems
The 40-point pre-deploy checklist, monitoring patterns, incident response, rollback strategies, and the full production maturity playbook.
AI Hallucination
Hallucination is often a symptom of concept drift or RAG staleness. This course covers detection, grounding, and the 30-point prevention checklist.
Prompt Patterns That Survive Production
When drift changes what your prompts need to say, these patterns keep output quality stable. Output contracts, versioning, and regression testing.
MLOps
Drift monitoring sits inside a broader MLOps practice: model registries, pipeline automation, CI/CD for ML, and experiment tracking.
AI Testing
Systematic testing before deployment reduces the blast radius when drift eventually occurs. Unit tests, integration tests, and evaluation harnesses for AI.
Token Optimization
Verbose output drift wastes tokens as well as degrading quality. The cost discipline layer that pairs with drift-prevention.
Go Deeper With Expert Courses
Recommended learning resources from our partners. Affiliate disclosure.
DataCamp - AI & Data Science
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DataCampedX - Top AI Courses
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