Databricks Community

Great share! Databricks really has built something special on the ML side — MLflow alone has become almost a default for experiment tracking, and pairing it with Model Registry and Feature Store makes the whole path from experimentation to productio...

Great questions—especially the distinction between Optuna’s trial-level parallelism and LightGBM’s intra-trial threading. The interaction with Spark autoscaling and data locality is also something I’d love to see documented with a concrete example. C...

@infinitylearnin, the above podcast link was changed; it's not working !! 

@kartheek_rao, you are on the right track.Since you are doing clustering of new articles, it's unsupervised learning; you need to understand the feature engineering part more and the EDA part with MLFlow experiments. You have model monitoring as well...

Image annotation is the process of adding labels or tags to images so an AI/ML model can understand. You as a human can see with your eyes what is present in the image for the AI to understand it. It requires training data to prepare training data. I...

Update — reproduced at minimum complexity, and the failure is unobservableWe stopped theorising and bisected with a deliberately trivial model: SimpleImputer + LogisticRegression, logged with fe.log_model, no custom code, on an online store created t...

@airbotsThe underlying issue is generally a metadata reconciliation lag where the Vector Search index status remains stuck displaying Syncing in Catalog Explorer even after the sync pipeline has completed. The metadata status reporting mechanism gene...

Yeah, I’d start simple with XGBoost or LightGBM and use AutoML for a quick baseline. Then just focus on the features that actually improve the results.

Hi, Could you provide a workspaceID and endpointID and any other details such as screenshots? Will check to see what I can find based on those details, ~Mo.