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From databricks-pack
Execute Databricks secondary workflow: MLflow model training and deployment. Use when building ML pipelines, training models, or deploying to production. Trigger with phrases like "databricks ML", "mlflow training", "databricks model", "feature store", "model registry".
How this skill is triggered — by the user, by Claude, or both
Slash command
/databricks-pack:databricks-core-workflow-bThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Build ML pipelines with MLflow experiment tracking, model registry, and deployment.
Build ML pipelines with MLflow experiment tracking, model registry, and deployment.
databricks-install-auth setupdatabricks-core-workflow-a (data pipelines)For full implementation details and code examples, load:
references/implementation-guide.md
| Error | Cause | Solution |
|---|---|---|
Model not found | Wrong model name/version | Verify in Model Registry |
Feature mismatch | Schema changed | Retrain with updated features |
Endpoint timeout | Cold start | Disable scale-to-zero for latency |
Memory error | Large batch | Reduce batch size or increase cluster |
For common errors, see databricks-common-errors.
Basic usage: Apply databricks core workflow b to a standard project setup with default configuration options.
Advanced scenario: Customize databricks core workflow b for production environments with multiple constraints and team-specific requirements.
npx claudepluginhub nickloveinvesting/nick-love-plugins --plugin databricks-packSearches MemPalace before answering questions about past work, people, projects, or prior decisions. Returns verbatim stored content instead of guessing from model memory.
Guides Payload CMS config (payload.config.ts), collections, fields, hooks, access control, APIs. Debugs validation errors, security, relationships, queries, transactions, hook behavior.
Implements vector databases with Pinecone, Weaviate, Qdrant, Milvus, pgvector for semantic search, RAG, recommendations, and similarity systems. Optimizes embeddings, indexing, and hybrid search.