Machine Learning / 20
Production ML
Follow models into pipelines, deployment, monitoring, drift detection, retraining, and governance.
Deployment is not the finish line. It is where the real experiment begins.
This is a knowledge index. Published titles open their articles; the remaining entries outline planned coverage.
Subtopics
- 20.01
From Notebook to Production
Planned guide: From Notebook to Production. Focus on intuition, assumptions, and a worked example.
- 20.02
Training Pipelines
Planned guide: Training Pipelines. Focus on intuition, assumptions, and a worked example.
- 20.03
Inference Pipelines
Planned guide: Inference Pipelines. Focus on intuition, assumptions, and a worked example.
- 20.04
Batch Inference
Planned guide: Batch Inference. Focus on intuition, assumptions, and a worked example.
- 20.05
Online Inference
Planned guide: Online Inference. Focus on intuition, assumptions, and a worked example.
- 20.06
Real-Time Prediction
Planned guide: Real-Time Prediction. Focus on intuition, assumptions, and a worked example.
- 20.07
Feature Stores
Planned guide: Feature Stores. Focus on intuition, assumptions, and a worked example.
- 20.08
Model Registry
Planned guide: Model Registry. Focus on intuition, assumptions, and a worked example.
- 20.09
Model Versioning
Planned guide: Model Versioning. Focus on intuition, assumptions, and a worked example.
- 20.10
Data Versioning
Planned guide: Data Versioning. Focus on intuition, assumptions, and a worked example.
- 20.11
Experiment Tracking
Planned guide: Experiment Tracking. Focus on intuition, assumptions, and a worked example.
- 20.12
Deployment Strategies
Planned guide: Deployment Strategies. Focus on intuition, assumptions, and a worked example.
- 20.13
Shadow Deployment
Planned guide: Shadow Deployment. Focus on intuition, assumptions, and a worked example.
- 20.14
Canary Deployment
Planned guide: Canary Deployment. Focus on intuition, assumptions, and a worked example.
- 20.15
A/B Testing Models
Planned guide: A/B Testing Models. Focus on intuition, assumptions, and a worked example.
- 20.16
Model Monitoring
Planned guide: Model Monitoring. Focus on intuition, assumptions, and a worked example.
- 20.17
Data Drift
Plan checks for changing input data and distinguish detection from a performance conclusion.
- 20.18
Concept Drift
Planned guide: Concept Drift. Focus on intuition, assumptions, and a worked example.
- 20.19
Prediction Drift
Planned guide: Prediction Drift. Focus on intuition, assumptions, and a worked example.
- 20.20
Performance Degradation
Planned guide: Performance Degradation. Focus on intuition, assumptions, and a worked example.
- 20.21
Retraining
Planned guide: Retraining. Focus on intuition, assumptions, and a worked example.
- 20.22
ML Observability
Planned guide: ML Observability. Focus on intuition, assumptions, and a worked example.
- 20.23
Model Governance
Planned guide: Model Governance. Focus on intuition, assumptions, and a worked example.
- 20.24
Reproducibility
Planned guide: Reproducibility. Focus on intuition, assumptions, and a worked example.
- 20.25
Production Failure Modes
Planned guide: Production Failure Modes. Focus on intuition, assumptions, and a worked example.