The machine learning hub is a platform to share pre-built Machine Learning and Artificial Intelligence models and Data Science best practices. Each package also wraps the corresponding functionality into a command that is able to be readily deployed within the traditional Unix/Linux command line pipelines.
MLHub links github 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 mlhub package 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.
If you have access to a computer running Ubuntu 18.04 LTS (e.g., Windows 10’s Subsystem for Linux) then getting started is simple. Install the command line tool to explore the rain, colorize, objects, and facedetect machine learning models, to start with. Have a look also at the data science visualisation packages, including animate and beeswarm. Explore the offerings from the Azure cloud like azcv and azspeech2txt But why not explore each one of them! There may be examples there that really interest you or could support your current projects and mobile applications.
MLHub works best on Ubuntu 18.04, 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 an Azure Ubuntu server or a Ubuntu instance on any cloud server.
Please feel free to contribute models or suggestions to mailto:email@example.com. It is easy to contribute your own model simply by creating a configuration for it called MLHUB.yaml and a demo.py or demo.R file on your github repository. Then simply point the ml command to that repository! As of March 2019 mlhub has been downloaded over 44,000 times.