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[air] add feast example (#25417)

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xwjiang2010 2022-06-07 14:55:42 -07:00 • committed by GitHub
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doc/source/_toc.yml
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@ -34,6 +34,7 @@ parts:
- file: ray-air/examples/rl_serving_example
- file: ray-air/examples/rl_online_example
- file: ray-air/examples/rl_offline_example
- file: ray-air/examples/feast_example
- file: ray-air/package-ref
- caption: AIR Libraries

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doc/source/ray-air/examples/feast_example.ipynb Normal file
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doc/source/ray-air/examples/index.rst
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@ -39,3 +39,4 @@ Advanced
--------
- :doc:`/ray-air/examples/torch_incremental_learning`: Incrementally train and deploy a PyTorch CV model
- :doc:`/ray-air/examples/feast_example`: Integrate with Feast feature store in both train and inference

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doc/source/ray-air/examples/tfx_tabular_train_to_serve.ipynb
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@ -15,7 +15,7 @@
"every step from data ingestion to pushing a model to serving.\n",
"\n",
"1. Read a CSV into [Ray Dataset](https://docs.ray.io/en/latest/data/dataset.html).\n",
"2. Process the dataset by chaining [Ray AIR preprocessors](https://docs.ray.io/en/master/ray-air/package-ref.html#preprocessors).\n",
"2. Process the dataset by chaining [Ray AIR preprocessors](https://docs.ray.io/en/latest/ray-air/getting-started.html#preprocessors).\n",
"3. Train the model using the TensorflowTrainer from AIR.\n",
"4. Serve the model using Ray Serve and the above preprocessors."
]
@ -453,14 +453,14 @@
"a modularized component so that the same logic can be applied to both\n",
"training data as well as data for online serving or offline batch prediction.\n",
"\n",
"In AIR, this component is a [`Preprocessor`](https://docs.ray.io/en/master/ray-air/package-ref.html#preprocessors).\n",
"In AIR, this component is a [`Preprocessor`](https://docs.ray.io/en/latest/ray-air/getting-started.html#preprocessors).\n",
"It is constructed in a way that allows easy composition.\n",
"\n",
"Now let's construct a chained preprocessor composed of simple preprocessors, including\n",
"1. Imputer for filling missing features;\n",
"2. OneHotEncoder for encoding categorical features;\n",
"3. BatchMapper where arbitrary user-defined function can be applied to batches of records;\n",
"and so on. Take a look at [`Preprocessor`](https://docs.ray.io/en/master/ray-air/package-ref.html#preprocessors).\n",
"and so on. Take a look at [`Preprocessor`](https://docs.ray.io/en/latest/ray-air/getting-started.html#preprocessors).\n",
"The output of the preprocessing step goes into model for training."
]
},