Databricks Community

Hi @Tingting ,It seems that when you run notebook interactively, your personal credentials are used to access ADLS.When the workflow job is run, Databricks uses different context. Could you share whether your job is accessing some storage account, an...

Hi @NielsMH ,By default, the Databricks CLI looks for the .databrickscfg file in your ~ (your user home) folder on Unix. You can try to delete this file and run configuration process again.You can also use describe command to check what credentials a...

No, most of the platforms, including services like Azure and Databricks, are not able to allow the creation of a machine learning compute cluster on this community edition. Most of these editions allow only basic features and resources enough to expl...

You would need to install the python library. You can either:1) Run %pip install datasets2) Put it as part of the PyPi packages to load in your cluster This should solve your issue

Hello! Did you try specify a column name as manual split column?  Then you can fully control which rows are in train / validate / test splits: https://docs.databricks.com/en/machine-learning/automl/automl-data-preparation.html#split-data-for-regressi...

Hey @Kumaran, I am using a Random forest classifier however I have tried to set the max depth to none since it is the default value but the error still exists. 

Hi  @Quinten,You can consider creating a custom feature group to store the "weight" column during training. This way, you can keep the schema of the TrainingSet consistent between training and inference time.Here are the steps you can follow:Create a...

@KumaranT I did it already with the same result import mlflow.pyfunc # Load the model as a PyFuncModel model = mlflow.pyfunc.load_model(model_uri=f"{model_version_uri}") # Create a Spark UDF for scoring predict_udf = mlflow.pyfunc.spark_udf(spark, ...