Andrei,
I am also working with Apache Ignite ML and am interested in providing
wrappers for Ignite ML API, but am wondering if instead of simply recreating
the low level Java API for ML inside Python, how about creating some higher
level services "Auto ML" workflow ? For example:
1. here is raw dataset, already inside this cluster cache "myName", with
Label column "MyLable" , take N samples tell me which appear to be numeric,
unique id, and categorical values?
2. based on N samples, , please run some analysis and tell me the top 5
feature columns in terms of predictive value using algorithm = RandonForest
3. do a batch run, sample size = N, using these preprocessing steps list
{impute, scale, etc} and algorithms (knn, Decision Tree, etc} and give me a
report of accuracies obtain with each.
In other words, we have a simple sample in the Tutorial demo where these
all run and then we compare outputs - why not automate these with a Python
Notebook UI of some sort?
--
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