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docs: Clean up examples folder (#5154)
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* Clean up examples folder

Signed-off-by: Sherlock113 <[email protected]>

* fix: move quickstart to test fixtures

Signed-off-by: Frost Ming <[email protected]>

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Signed-off-by: Sherlock113 <[email protected]>
Signed-off-by: Frost Ming <[email protected]>
Co-authored-by: Frost Ming <[email protected]>
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Sherlock113 and frostming authored Dec 26, 2024
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2 changes: 1 addition & 1 deletion docs/source/examples/mlflow.rst
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Expand Up @@ -4,7 +4,7 @@ MLflow

`MLflow <https://mlflow.org/>`_ is an open-source platform, purpose-built to assist machine learning practitioners and teams in handling the complexities of the machine learning process. MLflow focuses on the full lifecycle for machine learning projects, ensuring that each phase is manageable, traceable, and reproducible.

This document explains how to serve and deploy an MLflow model with BentoML. You can find all the source code in `examples/mlflow <https://github.com/bentoml/BentoML/tree/main/examples/mlflow>`_.
This document explains how to serve and deploy an MLflow model with BentoML. You can find all the source code `here <https://github.com/bentoml/BentoMLflow>`_.

Prerequisites
-------------
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5 changes: 2 additions & 3 deletions docs/source/examples/overview.rst
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Expand Up @@ -92,9 +92,8 @@ Custom models

Serve custom models with BentoML:

- `MLflow <https://github.com/bentoml/BentoML/tree/main/examples/mlflow>`_
- `XGBoost <https://github.com/bentoml/BentoML/tree/main/examples/xgboost>`_
- `scikit-learn <https://github.com/bentoml/BentoML/tree/main/examples/sk-learn>`_
- `MLflow <https://github.com/bentoml/BentoMLflow>`_
- `XGBoost <https://github.com/bentoml/BentoXGBoost>`_

Others
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2 changes: 1 addition & 1 deletion docs/source/examples/xgboost.rst
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Expand Up @@ -4,7 +4,7 @@ XGBoost

`XGBoost <https://xgboost.readthedocs.io/en/stable/>`_ is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework.

This document explains how to serve and deploy an XGBoost model for predicting breast cancer with BentoML. You can find all the source code in `examples/xgboost <https://github.com/bentoml/BentoML/tree/main/examples/xgboost>`_.
This document explains how to serve and deploy an XGBoost model for predicting breast cancer with BentoML. You can find all the source code `here <https://github.com/bentoml/BentoXGBoost>`_.

Prerequisites
-------------
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