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by Graham Williams
Duck Duck Go

MLHub Introduction

The machine learning hub (MLHub) is a framework and repository used as the platform through which the capabilities of AI, machine learning and data science are presented and accessible. It is a platform to share pre-built Machine Learning and Artificial Intelligence models and Data Science best practices. Each package wraps its functionality into a command that is able to be readily deployed within the traditional Unix/Linux pipelines.

A demo command is available with most packages to interactively demonstrate the capabilities of each package. A gui command provided by many packages presents a graphical user interface through which to use the particular capabilities of the package. The aim is to be able to download and install a package and then to run the demo within 5 minutes to determine whether the package is of interest to the user, or not.

MLHub links git based repositories into a collection of quickly accessible and ready to run, explore, rebuild, and even deploy, pre-built machine learning models and data science technology. The models and technology are accessed and managed using the ml command from the open source mlhub software. The software is available for quick installation from pypi. A growing number of machine learning models and data science technology are becoming available, as well as cloud based services.

Getting started is simple for computers running Ubuntu LTS (including the Windows 10’s Subsystem for Linux and the Raspberry Pi). The command line tool allows for the rapid exploration of data science capabilities including visualisations and animations, machine learning models for rain prediction and movie recommendation, and AI models to colorize photos, identify objects, and to detect faces.

MLHub works best on Ubuntu LTS (18.04 and 20.04), ideally on your own laptop. It is also really easy to install on Windows 10 through the Windows Subsystem for Linux or the Hyper-V gallery (enable Hyper-V and choose Ubuntu), and MacOS X (using Parallels or Virtual Box to install from the Ubuntu iso). It also runs well on any cloud Ubuntu server, including Azure.

Support further development by purchasing the PDF version of the book.
Other online resources include the Data Science Desktop Survival Guide.
Books available on Amazon include Data Mining with Rattle and Essentials of Data Science.
Popular open source software includes rattle and wajig.
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