ml-pipeline-setup
MLflow and ML Model patterns for Databricks including experiment creation, model training, batch inference, and Unity Catalog integration. Use when implementing ML pipelines, training models with Feature Store, or deploying batch inference jobs. Includes 19 non-negotiable rules covering experiment paths, dataset logging, UC model registration, NaN handling, label binarization, feature engineering workflows, and signature-driven preprocessing.
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A relentless interview to sharpen a plan or design, which also creates docs (ADR's and glossary) as we go.
