ML Monitoring & Observability

Detect drift, explain decisions, and prevent silent model failure

Build production-grade ML monitoring systems — data drift detection, model explainability, LLM observability, and CI/CD quality gates that catch regressions before users do.

10 levels
1
Why Monitoring MattersDiagnosis Lab

The 70% Rule — What Nobody Shows in Their Portfolio

+100 XP
2
Data Drift DetectionParameter Tuner

Statistical Methods — KS, PSI, and Wasserstein

+100 XP
3
Types of DriftConcept Matcher

Taxonomy — Know the Difference to Fix the Right Thing

+100 XP
4
Evidently AI in CI/CDPipeline Builder

Test Suites as Quality Gates — The pytest Trick

+100 XP
5
Model ExplainabilitySpeed Quiz

SHAP, LIME, and EU AI Act Legal Requirements

+100 XP
6
LLM-Specific MonitoringCost Optimizer

Tokens, Hallucinations, Latency — The New Monitoring Frontier

+100 XP
7
Tracing & Observability ToolsPipeline Builder

Langfuse + OpenTelemetry — Distributed Tracing for LLM Apps

+100 XP
8
CI/CD for ML — Regression GatingSpeed Quiz

Shadow Mode, Canary Deployments, and Golden Datasets

+100 XP
9
A/B Testing for MLParameter Tuner

Sample Sizes, Guardrail Metrics, and Thompson Sampling

+100 XP
10
Chapter Boss: Interview GauntletSpeed Quiz

20 Expert Monitoring Questions — Score 80% to Unlock Chapter 4

+200 XP