Introduction to MLOps
Machine Learning Operations (MLOps) is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently.
Why MLOps?
Scaling machine learning beyond a single notebook requires robust processes. MLOps bridges the gap between data science and software engineering.
Key Components
- Version Control: Tracking data, code, and model versions.
- Automation: Using CI/CD pipelines to automate testing and deployment.
- Monitoring: Observing model performance and detecting drift in production.
- Collaboration: Improving communication between data scientists and IT operations.
The MLOps Lifecycle
- Data Engineering: Ingesting and cleaning data.
- Model Development: Training and evaluating models.
- Deployment: Moving models to a production environment.
- Operations: Monitoring, logging, and retraining.