Getting ML models to production is different from training them. Most data scientists struggle with the operationalization phase.
The ML Lifecycle
1. Training in notebooks
2. Model validation
3. Containerization
4. Deployment and monitoring
5. Continuous improvement
Each step requires specific tools and practices.