Special issue: Deploy MLflow models on Baseten

Baseten now supports MLflow models via Truss. MLflow is a popular library for model experimentation and model management with over ten million monthly downloads on PyPi. With MLflow, you can train a model in any framework (PyTorch, TensorFlow, XGBoost, etc) and access features for tracking, packaging, and registering your model. And now, deploying to Baseten is a natural extension of MLflow-based workflows.

Deploying an MLflow model looks a bit like this:

import mlflow
import baseten
model = mlflow.pyfunc.load_model(MODEL_URI)
baseten.deploy(model, "MLflow model")

For a complete runnable example, check out this demo on Google Colab.

Baseten uses MLFlow's pyfunc module to load the model and packages it via Truss. To learn more about packaging MLflow models for deployment, consult the Truss documentation on MLflow.