gmanch94

bigquery-ml-design

gmanch94/claude-code-aidlc-template/.claude/skills/bigquery-ml-design/SKILL.md

Designs a BigQuery ML (BQML) footprint on Google Cloud — the in-warehouse, SQL-driven ML surface. Covers model-type selection (linear / logistic / boosted-tree XGBoost / DNN / k-means / ARIMA_PLUS / matrix-factorization / AutoML / imported TF+ONNX), Gemini-in-BigQuery (ML.GENERATE_TEXT, ML.GENERATE_EMBEDDING, VECTOR_SEARCH + vector indexes, remote models over Vertex endpoints), the BQML-vs-export-to-Vertex decision, on-demand vs slot/editions compute + model-creation vs prediction billing, MLOps (TRANSFORM clause for train/serve consistency, scheduled-query retraining, model versioning), and serving (in-warehouse ML.PREDICT vs Vertex endpoint export for online). Use when data already lives in BigQuery and the team is SQL-first, when scoping a warehouse-native ML workload, or when deciding whether a model should stay in BQML or graduate to Vertex. Adjacent to `/vertex-ai-design` (the export target + sibling), `/snowflake-cortex-design` (warehouse-native peer on Snowflake), and `/rag-design` (vector-search retrieval depth).