# Databricks Ml Training

> Train ML models on Databricks. Use for: classification/regression/deep-learning (XGBoost, scikit-learn, LightGBM, PyTorch) with Optuna, @prod/@challenger aliases, batch scoring (sparkudf for plain models, fe.scorebatch for feature-store-backed), custom PyFunc, custom ResponsesAgent (LangGraph + UC Function/Vector Search); UC feature tables + FeatureLookup + point-in-time joins + Lakebase online store; declarative Feature Views (createfeature, DeltaTableSource, RollingWindow/SlidingWindow/TumblingWindow, materializefeatures, streaming Kafka features). NOT for: endpoint ops (databricks-model-serving), MLflow evaluation (databricks-mlflow-evaluation).

## Facts
- Page: https://tashan.sh/capability/skill-databricks-databricks-ml-training
- tashan id: skill:databricks/databricks-ml-training
- Source: https://github.com/databricks/databricks-agent-skills
- Type: skill
- Category: ai
- tashan score: not scored (catalogued only — too little public evidence)
- Adoption: 9.0
- Upkeep: not measured
- Freshness: not measured
- Evidence coverage: not measured
- Health: not measured
- Instruction depth: not yet graded
- Official: no

## Install

```sh
cp -r databricks-ml-training ~/.claude/skills/
```

## Security audit
Not scanned. We audit npm-published capabilities; this one has no npm package we can resolve, or has not reached the queue. This is not a clean bill of health.

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Measured 2026-08-04 by tashan (https://tashan.sh) from public evidence. Scorer s5.