Collaboration

Once you have a machine learning model that's ready for production, getting it out can be complicated. In this tutorial, we're going to use MLEM to deploy a model as a web API.

We're going to set up a GCP storage bucket remote in a DVC project.

We're going to set up an Azure Blob Storage remote in a DVC project.

We're going to set up an AWS S3 remote in a DVC project.

We're going to look at how you can prevent stale models from remaining in production when the data starts to differ from the training data.

Sharing experiments with teammates can help you build models more efficiently.