MLOps (Machine Learning Operations) applies DevOps principles to the ML lifecycle — automating training, evaluation, deployment, and monitoring of models in production. Unlike traditional software, MLOps must manage both code and data artifacts, and must detect degradation from data distribution shifts rather than just software bugs.
Overview
mindmap
root((MLOps))
Model Registry
Version control for models
Stage transitions
Metadata and lineage
Artifact storage
MLflow Registry
Experiment Tracking
Hyperparameter logging
Metric tracking
Artifact versioning
Comparison and search
MLflow Weights and Biases
Drift Detection
Data drift - input shift
Concept drift - label shift
Prediction drift
Statistical tests
Evidently Whylogs
Retraining Pipelines
Trigger strategies
Continuous training
Champion-challenger
Evaluation gates
AB Testing for ML
Traffic splitting
Statistical significance
Business metrics
Online evaluation
MLOps Maturity Model
graph TD
subgraph Level0[Level 0 - Manual]
L0[Manual scripts\nNo pipeline\nModel in notebooks\nDeploy by hand]
style L0 fill:#fee2e2,stroke:#dc2626
end
subgraph Level1[Level 1 - ML Pipelines]
L1[Automated training pipeline\nExperiment tracking\nModel registry\nManual deployment trigger]
style L1 fill:#fef3c7,stroke:#d97706
end
subgraph Level2[Level 2 - CI/CD for ML]
L2[Automated retraining on trigger\nAutomated evaluation gate\nAutomated deployment\nProduction monitoring\nDrift detection]
style L2 fill:#dcfce7,stroke:#16a34a
end
Level0 -->|add pipeline + tracking| Level1
Level1 -->|add automation + monitoring| Level2
Topics in This Section
| File | Topic | Key Concepts |
|---|---|---|
| 01_model_registry.md | Model Registry | Versioning, stage transitions, lineage |
| 02_experiment_tracking.md | Experiment Tracking | Metrics, parameters, artifacts |
| 03_drift_detection.md | Drift Detection | Data drift, concept drift, PSI |
| 04_retraining_pipelines.md | Retraining Pipelines | Triggers, CT, evaluation gates |
| 05_ab_testing.md | A/B Testing for ML | Traffic splitting, significance |