Java in Machine Learning

Knoldus Blog Audio
Reading Time: 2 minutes

Overview

Machine Learning(ML) projects can be done in Java but there are some reasons why Java is not as popular as Python. Java is not the preferred first choice of Data Scientists and Machine Learning engineers for creating ML models.

Java is mainly used in large data processing and engineering parts of a typical ML life cycle. The processed and engineered data is used by the ML models for both supervised and unsupervised learning tasks. ML deals with large collections of data and datasets that need to be processed in an systematic and effective way. The quick learning curve in Python and the availability of frameworks allows more data scientists to quickly pick up Python.

Some common ML libraries available in Java.

Deeplearning4j

Deeplearning4j is an open-source, distributed and deep learning library for the JVM which is written in Java. It is compatible with any JVM language, such as Scala, Clojure or Kotlin.

TensorFlow-Java

TensorFlow provides a Java API. Though it is not as developed and stable as TensorFlow’s Python API it can run on JVM and has support for both CPU and GPU.

Apache OpenNLP

OpenNLP is an open source Natural Language Processing Java library. It has features for entity recognition, parts of speech detection and tokenization.

ADAMS

The Advanced Data Mining And Machine learning System (ADAMS) is a flexible workflow engine. It is used for quickly building and maintaining data-driven, reactive workflows, easily integrated into business processes.

One of the reasons Python has gained popularity is the availability of libraries and frameworks. There are frameworks and libraries for almost every aspect of machine learning. This makes Python more popular with Data Scientists and ML engineers.

Some popular frameworks and libraries for machine learning.

Tensorflow
For Machine Learning, Deep Learning and heavy computations.
Scikit-LearnFor handling complex data, clustering, linear and logistic regressions, classifications.
NumPyFor the computation of scientific or mathematical data.
TheanoFor computing mathematical expressions with multi-dimensional arrays.
KerasFor calculations and prototyping and offers functionalities for computing models, data-sets, visualising graphs, etc.
NLTKFor Natural Language recognition and processing, text analysis, and text mining.
PandasFor handling large data structures and analysis.
MatplotlibFor the creation of visualising objects such as 2D plots, histograms, and charts.

Rapid Model Development

ML projects require a lot of experimentation, evaluation and testing in an iterative and incremental manner. For a ML engineer to write simple block of scripts and executing them is quite easy using Python. Tools such as Jupyter Notebook and Google Colab also facilitate rapid model development. These tools make the life of a ML engineer easier and less complex.

Model Evaluation and Visualisation

Python offers a lot of libraries for data and model evaluation and visualisation. It is important to highlight that in machine learning, it is quite important to be able to represent and visualise data in a human understandable format.

Conclusion

With the advancement and availability of out of the box platforms to manage end to end data science life cycle projects, going forward this field of Machine Learning will become more automated and language agnostic or a low code /no code affair.

References

https://deeplearning4j.org/

https://opennlp.apache.org/

Written by 

Rohit Jagati is a technologist, experienced in designing enterprise grade solutions. Rohit has been designing and implementing solutions based on Machine Learning, IOT and custom/bespoke applications.