As the computing industry continues to innovate, the relationship between machine learning and operating systems has continued to mature. While there are a number of differences between Linux operating systems and Windows operating systems, both are used to some degree in modern machine learning applications. In this article we will discuss some of the computer operating systems most commonly used for machine learning and explore related technologies.
- Machine Learning and Operating Systems
- Machine Learning: Linux vs Windows
- Understanding Machine Learning Technologies
The Relationship Between Machine Learning and Operating Systems
Which OS Is Best for Machine Learning?
Modern machine learning algorithms generally perform their computational tasks within the core software that controls the entire computer system, also known as the kernel level. In theory, this means that the user-level operating system is largely irrelevant. In practice, however, the user-level OS used by the machine learning algorithm can make a world of difference. Without the ability to install the appropriate software packages and make necessary software configuration changes, you may find yourself unable to implement the machine learning strategy you would prefer. Another consideration is the monetary cost associated with your operating system of choice, as some require expensive software licenses to use. With all of these considerations at play, it is important to familiarize yourself with some of the more commonly used operating systems for machine learning applications.
Open Source vs Proprietary
Just as with any computer software, there is a distinction between Open Source and Proprietary operating systems. Generally speaking, open-source operating systems are easier to integrate into machine learning strategies as you can install specific software packages and configure the system to your liking. Perhaps most importantly, open-source operating systems are usually free to use and modify, making them extremely versatile and easy to access.
By contrast, proprietary software locks you into a set of pre-established software packages and system configurations. This can be a problem if your machine learning project requires the use of a specific software package that is not made available through the proprietary platform. The trade-off for this rigidity is that proprietary software typically comes with associated technical support and regular software updates, making it potentially more reliable than some open source counterparts. It is worth noting that the more widely used and trusted open source software resources are often just as reliable as proprietary software options, with the same degree of updates and support. Regardless, the potential reliability of proprietary software is offset by the monetary cost as this type of software generally requires the purchase of a valid software license, which can be cost prohibitive for some.
For more information on the differences between open source and proprietary software, please see the following article.
Machine Learning: Linux vs Windows
One of the most commonly used operating systems for machine learning is Linux. The open-source nature of Linux environments lends itself well to the complex installation and configuration processes required by many machine learning applications. Linux itself is made up of a wide variety of different distributions also known as distros, and some of these are better suited for machine learning than others. One of the main Linux distros used for machine learning is Ubuntu.
Ubuntu, a popular Linux distro, is uniquely suited for the use of machine learning applications due to the built-in support for a variety of programs used in the creation of machine learning applications. Software such as Kubernetes and Docker are fully compatible with Ubuntu, making it easier to get started with your machine learning project with fewer obstacles. Additionally, Linux is more secure than other Linux distros and provides for a more reliable experience overall.
Ubuntu also benefits from Linux’s smaller resource profile, meaning your system resources are more available for use in your machine learning project. This ultimately translates into fewer slowdowns and performance bottlenecks than you would encounter while using more bloated operating systems or Linux distros. Best of all Ubuntu is free to use, requiring no monthly fee or subscription. Based on all of the factors we’ve described, Ubuntu appears to be the best choice for machine learning applications.
While Windows remains a popular computer operating system, it is less commonly used for machine learning applications due to the proprietary nature of the software. Basic utilities for machine learning can be used in a Windows environment and standard algorithms can be implemented, but more advanced or customized configurations are not generally available.
The Windows operating system is great for users looking to perform simple machine learning operations in a stable environment, but less ideal for more complex applications. For this reason, Windows can serve as a useful learning tool for users looking to familiarize themselves with machine learning operations or for users looking to perform standardized machine learning tasks that don’t require a great deal of system configuration. Unlike Linux, Windows is a proprietary operating system and requires the purchase of a valid software license in order for use.
Everyday Uses of Machine Learning and Operating Systems
AI Assistants and Computer Operating Systems
While machine learning applications continue to be developed for a wide range of fields such as medicine and finance, machine learning is also used more commonly in the form of AI Assistants. Siri, Google Assistant, Cortana, and many others are examples of modern AI assistants installed on mobile devices and computers across the world. These incredibly complex programs use machine learning algorithms to better predict and understand many aspects of your day-to-day routine, from your physical location to your internet activity. Integrated seamlessly into modern operating systems, these AI assistants can help boost productivity, manage information, and facilitate communication between you and your contacts.
The Best Operating System for Machine Learning
Even though there are a number of different operating systems that can be used for machine learning, the clear choice for most users is Ubuntu. This software is uniquely suited for machine learning due to its compatibility with common programs such as Docker and provides a level of performance and security that is difficult to match with other operating systems.
Combined with the fact that it is a free piece of software, it is no surprise that Ubuntu is currently the operating system of choice for users working with machine learning applications. Now that you have a better understanding of machine learning and operating systems, hopefully you can more easily determine which operating system is right for your machine learning project.