Model Pipeline
Domain: MLOps
🎯 Motivation
The automated flow: data ingestion -> preprocessing -> training -> evaluation -> deployment -> monitoring.
📋 Concrete Example
Pipeline: 1) Fetch new data hourly. 2) Preprocess. 3) Retrain weekly. 4) Evaluate. 5) Deploy if better. 6) Monitor continuously. Fully automated ML lifecycle.
🔗 Analogy
An irrigation system - water flows from source -> filter -> distribute -> monitor -> adjust. Automated end-to-end. A model pipeline automates the ML lifecycle.