Overview
MLOps is to machine learning what DevOps is to traditional software. It focuses on the entire lifecycle of an ML model, from data collection to deployment and monitoring.
Key Components
- CI/CD for ML: Automating the building, testing, and deployment of models.
- Model Versioning: Tracking different versions of models and the data they were trained on.
- Monitoring: Tracking model performance in the real world to detect 'drift' (when the model becomes less accurate over time).
- Governance: Ensuring models are compliant, ethical, and secure.
Goal
To bridge the gap between data science (building models) and operations (running them in production).