MLOps
Keeping ML models running correctly in production: versioning, retraining, monitoring, and serving infrastructure.
Subject: Machine Learning · Roles: MLOps Engineer
Concepts
Model Versioning & Registries
Tracking every trained model artifact alongside the data and code that produced it, so results are reproducible.
Automated Retraining Pipelines
Automatically retraining models as new data arrives, with validation gates before a new version replaces the current one.
Model Monitoring & Drift Detection
Detecting when a production model's performance degrades because the world changed since it was trained.
Feature Stores
A shared, consistent source of features for both training and serving, avoiding train/serve skew.
Model Serving & Inference Infrastructure
The infrastructure choices behind getting predictions out of a trained model at production latency and scale.
Canary Releases & Rollback for Models
Rolling out a new model version safely — a small traffic slice first, with a fast path back to the previous version.