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Machine Learning on AIX

April 16, 2019

If you believe that machine learning is strictly for Linux, check out this IBM tutorial on installing and configuring Python machine learning packages on AIX:

Machine learning is a branch of artificial intelligence that helps enterprises to discover hidden insights from large amounts of data and run predictions. Machine learning algorithms are written by data scientists to understand data trends and provide predictions beyond simple analysis. Python is a popular programming language that is used extensively to write machine learning algorithms due to its simplicity and applicability. Many packages are written in Python that can help data scientists to perform data analysis, data visualization, data preprocessing, feature extraction, model building, training, evaluation, and model deployment of machine learning algorithms.

This tutorial describes the installation and configuration of Python-based ecosystem of machine learning packages on IBM AIX. AIX users can use these packages to efficiently perform data mining, data analysis, scientific computing, data plotting, and other machine learning tasks. Some of these Python machine learning packages are NumPy, Pandas, Scikit-learn, SciPy, and Matplotlib.

Because all these packages are Python based, the latest version of Python needs to be installed on the AIX system. YUM can be used to install Python on AIX or it can be directly installed from AIX toolbox. This tutorial talks about Python3 but same should work for Python2 as well. You need to have python3-3.7.1.-1 or later version of Python from AIX toolbox to run these machine learning packages.

In this tutorial, we use a Python package management tool called pip to install these machine learning packages on AIX. These packages are compiled as part of pip installation because binary versions of these packages for AIX are not available on the Python Package Index (PyPI) repository.

You'll also find detailed instructions for installing on your system. In addition, there are several related tutorials covering topics like the Scientific Computing Tools for Python, NumPy, Scikit Learn, Project Jupyter, and YUM on AIX.


Posted April 16, 2019| Permalink

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