What is MLOps?
Building a machine learning model in a Jupyter Notebook is relatively easy. Running it reliably in production, monitoring its predictions, and retrained it as real-world data changes is difficult. MLOps brings DevOps discipline to the machine learning lifecycle.
The MLOps Workflow
- Data Versioning: Tracking datasets using tools like DVC to guarantee reproducible model training.
- Model Registry: Storing, versioning, and reviewing trained models.
- Model Monitoring: Tracking prediction drift (when real-world data deviates from training data, causing accuracy to degrade).