Why do large generative models sometimes degrade in performance after multiple deployments

Question

With the help of proper code tell me Why do large generative models sometimes degrade in performance after multiple deployments?

Answer 1

Large generative models may degrade in performance after multiple deployments due to issues like model drift, resource exhaustion, or incorrect fine-tuning. Over time, updates or changes in the deployment environment (e.g., hardware, software, or data distribution) can lead to inconsistencies. 

Here is the code snippet you can refer to:

In the above code, we are using the following:

• Model Drift: Retrain models periodically to handle changes in data distribution.
• Consistent Environment: Use containerization (e.g., Docker) to ensure consistency across deployments.
• Version Control: Track model and environment versions to avoid mismatches.