How to correctly update a model: replace the old with the new or use a more reasonable approach?

Proper updating of a machine learning model usually does not involve simply replacing the old model with a new one. A more sensible approach includes:

• Incremental learning: if the model and algorithm support it, update it on new data without full retraining.
• A/B testing: run the new model in parallel with the old one to compare quality and performance.
• Performance monitoring: track model metrics in production to detect degradation in a timely manner.
• Model version control: store and manage model versions to enable rollback.

This approach minimizes risks associated with quality deterioration and ensures a smooth transition to the new model.

Example in Python using scikit-learn for incremental learning:

from sklearn.linear_model import SGDClassifier

# Initialize the model
model = SGDClassifier()

# Train on the first dataset
model.partial_fit(X_train_part1, y_train_part1, classes=np.unique(y_train_part1))

# Update the model with new data
model.partial_fit(X_train_part2, y_train_part2)

If incremental learning is not possible, then it is important to thoroughly test the new model before replacing and have a rollback plan.