Databricks Community

Hi, I think this may be a duplicate of another question, but posting the same answer here for transparency: Hi, first things to check is that you have the correct permissions on the user or service principal you're running the job with, the user need...

Hi, first things to check is that you have the correct permissions on the user or service principal you're running the job with, the user needs to have workspace access and cluster creation access toggled on. Next you need to check you have a metast...

The practical differences between bagging and boosting mostly come down to how they build models and how they handle errors:Model Training Approach:Bagging (Bootstrap Aggregating): Builds multiple models in parallel using random subsets of the data. ...

Improving the performance of a Random Forest model on Databricks is usually about data quality, feature engineering, and hyperparameter tuning. Some tips:Feature Engineering:Create meaningful features and remove irrelevant ones.Encode categorical var...

@Suheb , You may look at the torch distributor. It provides multiple distributed training options, including single-node with multiple-GPU training and multi-node training. Below are the references for you. https://docs.databricks.com/aws/en/machine-...

Hi @Suheb , MLFlow is already pre installed in ML runtime. The question is very vague. You can follow the below documentations to get started with MLFlow on databricks. 1) https://www.databricks.com/product/managed-mlflow2) https://docs.databricks.co...

Hey @Suheb , I teach a lot of our machine learning training, and over time I’ve talked with many students, customers, and partners about how they approach this. The answers are all over the map, which tells you there’s no single “golden rule” that fi...

I'm afraid I don't have much further suggestions. I'd suggest you raise a ticket with Microsoft on this.

Workspace model registry worked with workspace-scoped serving endpoints. UC models and UC serving endpoints use metastore-wide semantics and different lookup rules. The saved path inside the model metadata still points to workspace-level endpoints th...

You can use UC's centralized model registry and MLflow’s copy APIs. If all target workspaces attach to the same Unity Catalog metastore, reference and promote models via their 3‑level UC names; use MLflow’s copy_model_version to “copy” the exact arti...