TheNeuralBit commented on code in PR #21803:
URL: https://github.com/apache/beam/pull/21803#discussion_r894921012
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sdks/python/apache_beam/ml/inference/sklearn_inference.py:
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@@ -94,9 +91,6 @@ class SklearnModelHandlerPandas(ModelHandler[pandas.DataFrame,
BaseEstimator]):
""" Implementation of the ModelHandler interface for scikit-learn that
supports pandas dataframes.
-
- NOTE: This API and its implementation are under development and
- do not provide backward compatibility guarantees.
Review Comment:
To be more specific on why this might change - now that the batching DoFn
infrastructure is in, I'd like to make the pandas sklearn implementation to
leverage it. We'd move to a model where the element type is a Beam Row (with
schema), and the batch type is a pandas DataFrame. As opposed to the current
model where the batch type is a list of single element dataframes.
Once we do that we could pass data from the DataFrame API (under the hood a
`PCollection[pd.DataFrame]`) directly to RunInference, without having to
unbatch it and then batch it back up.
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