Our latest product additions and improvements.

Feb 3, 2023

Real-world model deployment workflows can be a bit messy. The latest version of the Baseten Python client, version 0.3.0, gives you a cleaner path to production by introducing a live reload workflow to every model deployment. Live reload lets you test changes to your model in seconds rather than waiting for Docker to rebuild and your model to be redeployed.

Now, every deployed model starts as a draft model by default. Draft models support live reload for:

  • Updates to model serving code

  • Updates to required Python packages

  • Updates to required system packages

And other changes, like editing environment variables and updating your model binary, can still be made in the draft stage without incrementing a model version, though they will require waiting for a full redeployment.

Live reload saves time when iterating on draft models

Once you’re satisfied with your model, you can publish it to production resources. To publish your model, just pass publish=True as an argument to baseten.deploy(). You can also publish your model in the Baseten UI.

You can use this flag during your initial deployment to skip the draft model step if desired:

For a complete guide to deploying models, read our newly refreshed model deployment docs.