Databricks

Model storage requirements management

Hi.We have around 30 models in model storage that we use for batch scoring. These are created at different times by different person and on different cluster run times.Now we have run into problems that we can't de-serialize the models and use for in...

Are UDFs necessary for applying models from ML libraries at scale ?

Hello,I recently finished the "scalable machine learning with apache spark" course and saw that SKLearn models could be applied faster in a distributed manner when used in pandas UDFs or with mapInPandas() method. Spark MLlib models don't need this k...

Parallelization in training machine learning models using MLFlow

I'm training a ML model (e.g., XGboost) and I have a large combination of 5 hyperparameters, say each parameter has 5 candidates, it will be 5^5 = 3,125 combos.Now I want to do parallelization for the grid search on all the hyperparameter combos for ...

Two or more different ml model on one cluster.

Hi, have you already dealt with the situation that you would like to have two different ml models in one cluster? i.e: I have a project which contains two or more different models with more different pursposes. The goals is to have three differ...

Is it possible to load MLFlow artifacts and models from local diretory to databricks DBFS?

I have been working locally and created a few models and now I want to move those to databricks/DBFS. Is it possible to do that?

Should I be saving my SparkML models in MLflow using MLeap?

There's a lot of different ML formats out there and I am confused about how they should be fitting together. How should I be thinking about MLflow and MLeap working together?

How do you track models in one workspace but registry the model in another one?