Standardizing Workflows with MLflow Recipes

MLflow Recipes (formerly MLflow Pipelines) are opinionated templates for common ML tasks like regression and classification.

1. Why Recipes?¶

Structure: Every project follows the same steps (ingest -> split -> transform -> train -> evaluate).
Caching: If you only change the training parameters, Recipes will skip the data ingestion and transformation steps.
Best Practices: Templates include built-in evaluation plots and metrics.

from mlflow.recipes import Recipe

# Initialize a regression recipe
# r = Recipe(profile="local")
# r.run("train")
# r.inspect("evaluate")