How are production ML systems typically handling distribution shift over time? [D]

In deployed ML systems, data distribution drift seems unavoidable over longer time horizons.

I’m trying to understand what approaches are commonly used in practice:

• Continuous retraining pipelines (fixed intervals vs trigger-based)
• Online monitoring for feature or prediction drift
• Use of shadow models or fallback models in production
• Human-in-the-loop review for edge cases

In most real deployments I’ve seen discussed, retraining strategy seems more operationally constrained than model-related.

Curious what approaches are actually working reliably in production environments and what tends to fail first.