31 lines
1.4 KiB
Python
31 lines
1.4 KiB
Python
"""
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.. warning::
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MLflow Recipes is deprecated and will be removed in a future release.
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MLflow Recipes is a framework that enables you to quickly develop high-quality models and deploy
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them to production. Compared to ad-hoc ML workflows, MLflow Recipes offers several major benefits:
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- **Recipe templates**: `Predefined templates <../../recipes/index.html#recipe-templates>`_ for
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common ML tasks, such as `regression modeling <../../recipes/index.html#regression-template>`_,
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enable you to get started quickly and focus on building great models, eliminating the large amount
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of boilerplate code that is traditionally required to curate datasets, engineer features, train &
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tune models, and package models for production deployment.
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- **Recipe engine**: The intelligent recipe execution engine accelerates model development by
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caching results from each step of the process and re-running the minimal set of steps as changes
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are made.
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- **Production-ready structure**: The modular, git-integrated `recipe structure
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<../../recipes/index.html#recipe-templates-key-concept>`_ dramatically simplifies the handoff from
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development to production by ensuring that all model code, data, and configurations are easily
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reviewable and deployable by ML engineers.
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For more information, see the `MLflow Recipes overview <../../recipes/index.html>`_.
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"""
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from mlflow.recipes.recipe import Recipe
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__all__ = ["Recipe"]
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