| import mlflow.pyfuncdef deploy_model(model, model_path): """ Deploy the model using MLflow. """ # Save the model mlflow.pyfunc.save_model(model_path=model_path, python_model=model) # Deploy the model to a serving endpoint mlflow.pyfunc.log_model(artifact_path=model_path, python_model=model) print(f"Model deployed at {model_path}")# Example usagetrained_model = train_full_finetune_model(train_data, val_data)deploy_model(trained_model, 'models/deployed_model') | |