I am exploring PyFlink and I wonder if it is possible to use PyFlink together with all these ML libs that ML engineers normally use: PyTorch, Tensorflow, Scikit Learn, Xgboost, LightGBM, etc.
According to this SO thread, PySpark cannot use Scikit Learn directly inside UDF because Scikit Learn algorithms are not implemented to be distributed, while Spark runs distributedly.
Given PyFlink is similar to PySpark, I guess the answer maybe "no". But I would love to double check, and to see what I need to do to make PyFlink able to define UDFs using these ML libs.
Thanks for the investigation of PyFlink together with all these ML libs. IMO, you could refer to the flink-ai-extended project that supports the Tensorflow on Flink, PyTorch on Flink etc, which repository url is https://github.com/alibaba/flink-ai-extended. Flink AI Extended is a project extending Flink to various machine learning scenarios, which could be used together with PyFlink. You can also join the group by scanning the QR code involved in the README file.