20200901 The machine learning hub (MLHub) is a framework and repository, through which the capabilities of Machine Learning, Artificial Intelligence, and Data Science are presented and accessible. Pre-built Machine Learning and Artificial Intelligence models as well as Data Science best practices are presented as packages. Each package wraps its functionality into commands that are able to be readily deployed within traditional and powerful Unix/Linux command line pipelines.
MLHub exposes a git software repository as a collection of quickly accessible and ready to run, explore, rebuild, and even deploy, pre-built machine learning models and data science technology. A growing number of machine learning models and data science technology are becoming available, as well as cloud based services.
Each MLHub package provides a demo command to interactively demonstrate the capabilities of the package. Many packages also include a gui command (graphical user interface) through which to explore the capabilities of the package. A collection of command line oriented commands are then provided by each package to enable the user to explore and utilise the capabilities of AI and Machine Learning algorithms.
Whilst enabling the power of the command line is an important goal of MLHub, the usefulness and relevance to an end user of the capabilities of the package must be demonstrable within about 5 minutes. The user can then decide whether the package supports something useful for them by which they can delve more deeply, or move on to something more interesting, having lost only 5 minutes of their time.
Your donation will support ongoing availability and give you access to the PDF version of this book. Desktop Survival Guides include Data Science, GNU/Linux, and MLHub. Books available on Amazon include Data Mining with Rattle and Essentials of Data Science. Popular open source software includes rattle, wajig, and mlhub. Hosted by Togaware, a pioneer of free and open source software since 1984. Copyright © 1995-2022 Graham.Williams@togaware.com Creative Commons Attribution-ShareAlike 4.0