Serving Supervised ML Models
Predibase supports the serving of custom (non-LLM) models for real-time inference.
Via the SDK
Once you're satisfied with your model's performance, you can deploy it to a REST API endpoint using the
rotten_tomatoes_deployment = pc.create_deployment('rotten_tomatoes_deployment', rotten_tomatoes_model)
Now that you've deployed your model, you can easily make predictions using the
preds_df = rotten_tomatoes_deployment.predict(rotten_tomatoes_df.sample(100), stream=True)
With this command, you can pass in any unseen data that you want predictions on, and Predibase will return a dataframe with the predictions for each example.
Via the UI
Learn how to create a supervised ML (non-LLM) deployment in the Predibase UI.