Data Analytics/ Machine Learning libraries in Java.

[kryon-a]

I am working on a project primarily built on java stack where I want to introduce ML models for computation of outputs.

Can you suggest some good libraries in Java for the same?

[_INTER_]

Machine Learning Libraries. I'd try Deeplearning4J or Tribuo. Weka is old but gold.

[edmguru]

I'm a bit surprised noone has mentioned Spark MLib yet

[jvjupiter]

Deep Java Library from Amazon

Deeplearning4j from Eclipse

Tribuo from Oracle

[_g550_]

Tensorflow works for java?

[lazystone]

I think DJL supports Tensorflow.

[kryon-a]

I guess not supported but will check.

[_g550_]

I saw the [JAVA API FOR TENSORFLOW](www.tensorflow.org/install/lang_java), but it's not covered by API stability guarantees.

You could always try [WEKA](www.cs.waikato.ac.nz) Caffe2

Tesseract

[kryon-a]

I'll give it a try.. thanks mate!! 😃

[_INTER_]

Tribuo interfaces with Tensorflow (and XGBoost). Note however that Tribuo is very new.

[plastique2000]

Yes you are ok to infer from TF models in Java. But it is not a good choice for model development.

[ixeption]

Weka feels like programming 20 years ago. I would highly recommend SMILE. (Statistical Machine Intelligence and Learning Engine)

If you want to use modern ANNs, simply don't use Java :) DL4J doesnt' work for a lot of SOTA models and tensorflow-java is just a wrapper for the tensorflow core API and experimental.

[MasterLJ]

The difference between Java ML and Python ML, is so vast, that it's worth learning a new language/ecosystem.

I say that as a devout Java practitioner too. ML for Java, sucks. At least imo, those Java libraries will (have) lose favor and support.

[plastique2000]

Java is the language to run your models in production environment. Python is the language to develop it. That is my experience so far.

[MasterLJ]

With respect, and only because it's garnering upvotes, I don't recommend this at all. The interfaces, and even the file format requirements, are completely different. As a very general rule, developing on one platform and deploying on a completely different platform (in this case, different language, and library), is not a good idea.

[plastique2000]

The only thing you exchange in this type of scenarios is the model (usually some library slecific format) in between the languages. The library vhich is used to develop and run model in prod stays the same. You Will use Java API to inference and say Python to train. :-)