What does the MLOps pipeline include from training to deployment?

The MLOps pipeline from training to production includes several key stages:

1. Data collection and preparation — cleaning, transforming, and augmenting data.
2. Model development and training — selecting architecture, tuning hyperparameters, training on training data.
3. Validation and testing — evaluating model quality on validation and test sets, checking for overfitting.
4. Versioning of models and data — storing artifacts with metadata for reproducibility.
5. Automation of CI/CD for models — automatic training, testing, and deployment.
6. Model deployment to production — deploying the model as a service or integrating into an application.
7. Monitoring and logging — tracking model performance, quality metrics, and failures.
8. Updating and retraining — regularly updating the model with new data to maintain quality.

This is roughly how the complete MLOps cycle looks, ensuring stable and manageable operation of an ML system in production.