Data Science

What is the Machine Learning Platform?

You know that Artificial Intelligence is today a laughing stock of the future, if you add this field to your list as a career skill and strive to acquire a power that will ultimately destroy humanity, you will likely be pretty rich. While these rhetoric may excite everyone at first, the next step is usually to enthusiastically seek to learn more about the field by everyone. You can start this job by enrolling in a course that you can usually progress online (in the style of udemy), and in this process, there are dozens of machine learning platforms that do not leave us alone, and you will provide a more permanent learning by trying machine learning methods on these platforms. In this article, I will not compare the platforms, but I will try to present one of them that I think is good.

Figure: Tools that appeared in a short time.

H20 is an open source, fast and scalable machine learning and predictive analytics program written in java programming language that allows us to create machine learning models on big data and enables these models to be easily produced in the corporate environment.

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Figure : location of between platforms.

When we look at the image above, the place of h20 among other machine learning platforms is shown. Compared to the others, we can say that it has been one of the best due to its new and existing machine learning algorithms and speed.

In the H20, data is read in parallel and distributed across the cluster and stored in memory in a compressed form in a column format. The data parser part of the H20 has built-in intelligence to predict the schema of the incoming dataset and supports data from multiple sources in various formats. H2O’s REST API allows access to all features of H2O from an external program or over HTTP to script over JSON. Rest API is used over h20’s web interface (User – Interface), H20-R, H20 – Python.

With its distributed memory architecture, the H20 allows machine learning models to be trained quickly. Each access to memory takes around 150 nanoseconds. In addition, you can save a model we trained with h20 as a java class and integrate it with any server we want with java support.

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Figure: H2O startup screen.

Models Included;

  • Supervised Learning (Advised): Deep Learning (Neural Networks), Distributed Random Forest (DRF), Generalized Linear Model (GLM), Gradient Boosting Machine (GBM), Naïve Bayes Classifier, Stacked Ensembles, XGBoost
  • Unsupervised Learning: Aggregagtor, Generalized Low Rank Models (GLRM), K-Means Clustering, Principal Component Analysis (PCA)
  • Other: Quantiles, Early Stopping, Word2Vec


Operating Systems:

  • Windows 7 or higher
  • OS X 10.9 or later
  • Ubuntu 12.04
  • Available for RHEL / CentOS 6 or later.


  • Scale
  • R
  • Python
  • It is not mandatory unless you want to use it in these 3 languages environment
  • Java language is always required.

Supported versions include:

  • Java 7 or higher
  • A 64-bit JDK is required to build H2O or run H2O tests.
  • Only 64-bit JRE is required to run the H2O binary using command line, R or Python packages.
  • Both of these are available on Java’s download page.
  • Scala 2.10 or higher
  • R version 3 or higher
  • Python 2.7.x, 3.5.x, 3.6.x


An internet browser is required to stream H2O’s web interface. Supported versions include the latest versions of Chrome, Firefox, Safari or Internet Explorer.

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