What are the main challenges in model retraining?

What are the main challenges in model retraining?

Some key challenges include data drift, computational costs, and maintaining model performance.

Can you explain data drift and why it's a problem?

Data drift occurs when the statistical properties of the input data change over time, leading to decreased model accuracy.

How can we mitigate the effects of data drift?

You can use techniques like continuous monitoring, retraining schedules, and adaptive learning to address data drift.

What about the computational costs? How can they be managed?

Optimizing algorithms, using cloud resources, and efficient data handling can help manage computational costs during retraining.