mlflow.mleap.log_model(spark_model=model, sample_input=test_data.limit(1), artifact_path=SAGEMAKER_APP_NAME), and then doing a deploy to SageMaker. But when i use boto3 to make the prediction call, the SageMaker endpoint only returns the prediction label 1 or 0, without the probability value. Is there anywhere i can look into to debug this problem?
mleap-spark_2.3.0, seriazation pipelne mode in local environment is success, while failed on cluster mode
mleap-xgboost-sparkoverwrite reference.conf in
Hello everybody :)
I am new to this chat. I was reading the MLeap documentation and I really think it's a great product. My only concern is that (as far as I know) it's not possible to use Spark-NLP annotators or any other python NLP package. Did anybody manage to build a pipeline with a Lemmatizer or any other processing step which is not included in default Spark ML or sklearn modules?