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MlFlow and Feature Store: mlflow.spark.autolog, using feature store on Databricks, FeatureStoreClient.log_model()?

Edmondo
New Contributor III

As I am moving my first steps within the Databricks Machine Learning Workspace, I am getting confused by some features that by "documentation" seem to overlap.

Does autolog for spark on mlflow provide different tracking than using a training set created via a feature store client? Also, how does FeatureStoreClient.log_model() relate with MLFlow?

1 ACCEPTED SOLUTION

Accepted Solutions

Kaniz
Community Manager
Community Manager

Hi @Edmondo Porcu​ , FeatureStoreClient.log_model() logs an MLflow model packaged with feature lookup information.

Source

mlflow.spark.autolog(disable=Falsesilent=False) enables (or disables) and configures logging of Spark data source paths, versions (if applicable), and formats when they are read.

Source

View solution in original post

3 REPLIES 3

Kaniz
Community Manager
Community Manager

Hi @ Edmondo! My name is Kaniz, and I'm the technical moderator here. Great to meet you, and thanks for your question! Let's see if your peers in the community have an answer to your question first. Or else I will get back to you soon. Thanks.

Kaniz
Community Manager
Community Manager

Hi @Edmondo Porcu​ ,

Are you referring to this documentation here?

https://www.mlflow.org/docs/latest/python_api/mlflow.spark.html

Kaniz
Community Manager
Community Manager

Hi @Edmondo Porcu​ , FeatureStoreClient.log_model() logs an MLflow model packaged with feature lookup information.

Source

mlflow.spark.autolog(disable=Falsesilent=False) enables (or disables) and configures logging of Spark data source paths, versions (if applicable), and formats when they are read.

Source

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