MLOps
In plain terms
Practices for deploying and maintaining ML models in production.
Going deeper
MLOps applies DevOps principles to machine learning, covering data pipelines, training, deployment, monitoring, and retraining. It operationalizes ML reliably.
Under the hood
Handles model versioning, drift monitoring, and reproducibility, harder than software due to data dependence.
Examples
Model deployment, monitoring, CI/CD for ML, MLflow, drift detection.
Think of it like...
Like a full logistics operation keeping AI models running smoothly after launch.