Log the model as a pickle object, upload the object to Unity Catalog Volume, register it to Unity Catalog using MLflow, and start a serving endpoint

Save the model along with its dependencies in a local directory, build the Docker image, and run the Docker container

Wrap the LLM’s prediction function into a Flask application and serve using Gunicorn