.. _sphx_glr_auto_examples_data_ops:

.. _data_ops_examples_ref:

Skrub DataOps
=================



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    <div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how we can use Skrub&#x27;s user_guide_data_ops_index to build a machine learning pipeline that records all the operations involved in pre-processing data and training a model. We will also show how to save the model, load it back, and then use it to make predictions on new, unseen data.">

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  .. image:: /auto_examples/data_ops/images/thumb/sphx_glr_1110_data_ops_intro_thumb.svg
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  :doc:`/auto_examples/data_ops/1110_data_ops_intro`

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      <div class="sphx-glr-thumbnail-title">Introduction to machine-learning pipelines with skrub DataOps</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how to build a DataOps plan to handle pre-processing, validation and hyperparameter tuning of a dataset with multiple tables.">

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  .. image:: /auto_examples/data_ops/images/thumb/sphx_glr_1120_multiple_tables_thumb.svg
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  :doc:`/auto_examples/data_ops/1120_multiple_tables`

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      <div class="sphx-glr-thumbnail-title">Multiples tables: building machine learning pipelines with DataOps</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="A machine-learning pipeline typically contains values or choices which may influence its prediction performance, such as hyperparameters (e.g., the regularization parameter alpha of a RidgeClassifier, the learning_rate of a HistGradientBoostingClassifier), which estimator to use (e.g., RidgeClassifier or HistGradientBoostingClassifier), or which steps to include (e.g., should we join a table to bring additional information or not).">

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  .. image:: /auto_examples/data_ops/images/thumb/sphx_glr_1130_choices_thumb.svg
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  :doc:`/auto_examples/data_ops/1130_choices`

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      <div class="sphx-glr-thumbnail-title">Hyperparameter tuning with DataOps</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use Optuna to tune the hyperparameters of a skrub DataOp. As seen in the previous example, skrub DataOps can contain &quot;choices&quot;, objects created with choose_from, choose_int, choose_float, etc. and we can use hyperparameter search techniques to pick the best outcome for each choice. Performing this search with Optuna allows us to benefit from its many features, such as state-of-the-art search strategies, monitoring and visualization, stopping and resuming searches, and parallel or distributed computation.">

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  .. image:: /auto_examples/data_ops/images/thumb/sphx_glr_1131_optuna_choices_thumb.svg
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  :doc:`/auto_examples/data_ops/1131_optuna_choices`

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      <div class="sphx-glr-thumbnail-title">Tuning DataOps with Optuna</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Here we show how to use DataOp.skb.subsample to speed up interactive construction of a skrub DataOps plan by computing previews on a subsampled version of the original data.">

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  .. image:: /auto_examples/data_ops/images/thumb/sphx_glr_1140_subsampling_thumb.svg
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  :doc:`/auto_examples/data_ops/1140_subsampling`

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      <div class="sphx-glr-thumbnail-title">Subsampling for faster development</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Use case: developing locally and deploying to production">

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  .. image:: /auto_examples/data_ops/images/thumb/sphx_glr_1150_use_case_thumb.svg
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  :doc:`/auto_examples/data_ops/1150_use_case`

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      <div class="sphx-glr-thumbnail-title">Use case: developing locally and deploying to production</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to wrap a PyTorch model with skorch and plug it into a skrub DataOps plan.">

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  .. image:: /auto_examples/data_ops/images/thumb/sphx_glr_1160_pytorch_thumb.svg
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  :doc:`/auto_examples/data_ops/1160_pytorch`

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      <div class="sphx-glr-thumbnail-title">Using PyTorch (via skorch) in DataOps</div>
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.. toctree::
   :hidden:

   /auto_examples/data_ops/1110_data_ops_intro
   /auto_examples/data_ops/1120_multiple_tables
   /auto_examples/data_ops/1130_choices
   /auto_examples/data_ops/1131_optuna_choices
   /auto_examples/data_ops/1140_subsampling
   /auto_examples/data_ops/1150_use_case
   /auto_examples/data_ops/1160_pytorch