This would be a useful feature.
We can leverage it while doing bulk provisioning.
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Hi, does anyone know how Spark's model serialization format can support feature
evolution with backward compatibility support? Specifically when new data that
include both old features and newly added features is fed into old models
trained with old set of features, the old models should be able
Thanks Holden and Chetan.
Holden - Have you tried it out, do you know the right way to do it?
Chetan - yes, if we use a Java NLP library, it should not be any issue in
integrating with spark streaming, but as I pointed out earlier, we want to
give flexibility to data scientists to use the language
But you can still use Stanford NLP library and distribute through spark
right !
On Sun, Nov 26, 2017 at 3:31 PM, Holden Karau wrote:
> So it’s certainly doable (it’s not super easy mind you), but until the
> arrow udf release goes out it will be rather slow.
>
> On Sun, Nov 26, 2017 at 8:01 AM a
So it’s certainly doable (it’s not super easy mind you), but until the
arrow udf release goes out it will be rather slow.
On Sun, Nov 26, 2017 at 8:01 AM ashish rawat wrote:
> Hi,
>
> Has someone tried running NLTK (python) with Spark Streaming (scala)? I
> was wondering if this is a good idea a
