What are the best practices for deploying machine learning models?

What are the best practices for deploying machine learning models?

Some best practices include versioning your models, monitoring performance, and ensuring reproducibility.

How important is model monitoring after deployment?

Model monitoring is crucial as it helps detect drift and ensures the model continues to perform well over time.

What tools can I use for monitoring my ML models?

You can use tools like Prometheus, Grafana, or specialized ML monitoring platforms like Fiddler or Arize.

Can you explain model versioning?

Model versioning involves keeping track of different iterations of your model, which helps in managing updates and rollbacks.