Model pipelines trained with Ludwig can be served by spawning a Rest API using the FastAPI library.

Let's serve the model we just created.

ludwig serve --model_path ./results/experiment_run/model
docker run -t -i --mount type=bind,source={absolute/path/to/rotten_tomatoes_data},target=/rotten_tomatoes_data ludwigai/ludwig serve --model_path /rotten_tomatoes_data/results/experiment_run/model

Now that our server is up and running, you can make a POST request on the endpoint to get predictions back:

curl -X POST -F "movie_title=Friends With Money" -F "content_rating=R" -F "genres=Art House & International, Comedy, Drama" -F "runtime=88.0" -F "top_critic=TRUE" -F "review_content=The cast is terrific, the movie isn't."

Since the output feature is a binary type feature, the output from the POST call will look something like this:

   "review_content_predictions": false,
   "review_content_probabilities_False": 0.76,
   "review_content_probabilities_True": 0.24,
   "review_content_probability": 0.76


Users can also send POST requests to the /batch_predict endpoint to run inference on multiple examples at once. Read more about ludwig serve to learn more about ludwig deployments.