<jupyter_start><jupyter_text>Then serve the model in the mlflow tracking container using docker exec ekholabs-mlflow /bin/sh -c "mlflow models serve -m /ekholabs-mlflow/mlruns/0/a85ab97a393045afaea2b550a79686e8/artifacts/model --host=0.0.0.0 -p 2349"<jupyter_code>! curl -X POST -H "Content-Type:application/json; format=pandas-split" --data '{"columns":["alcohol", "chlorides", "citric acid", "density", "fixed acidity", "free sulfur dioxide", "pH", "residual sugar", "sulphates", "total sulfur dioxide", "volatile acidity"],"data":[[12.8, 2, 10, 0.98, 1, 45, 99, 1.2, 44, 75, 0.66]]}' http://ekholabs-mlflow:2349/invocations<jupyter_output>[4.945446608985396]
{'fraction_non_alphanumeric': 0.17246596066565809, 'fraction_numerical': 0.10892586989409984, 'mean_word_length': 6.042553191489362, 'pattern_counts': {'":': 2, '<': 4, '<?xml version=': 0, '>': 4, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 1, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
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