Linux is widely considered one of the most prominent operating systems in the world. Its open-source nature is just one of many factors that led to Linux becoming this popular for businesses and regular customers. Not only can Linux be used to operate smartphones, personal computers, and servers, but it is also fast, secure, and flexible – making it an excellent choice for multiple forms of AI development.
Linux is an open-source offering, first and foremost, which makes it highly customizable with the right skill set. It may not be easy for some users, but customizing a Linux operating system for a very specific task, such as Artificial Intelligence development, is not particularly difficult.
The flexibility of Linux as a system is another significant contributor to it being a suitable development system – since it works with a large variety of devices, from small single-purpose hardware to complex data centers and infrastructures. The ever-rising number of data breaches and ransomware variations makes security a top priority for most companies in the world, and many AI-oriented topics often use large data amounts in their work, creating a rather significant security risk. Luckily, Linux is a highly secure platform by design, which is an advantage that is hard to overestimate in this day and age.
The aforementioned open-source nature of Linux is the main reason why this OS has so many different distributives available right now. A lot of these distributives are not particularly popular, but there are also some that have a substantial level of popularity – such as Fedora, Ubuntu, Debian, or CentOS.
Fedora is a Linux distribution focusing significantly on new and developing features. It is well-known for its strive to include all kinds of cutting-edge technologies in the distribution – something that is practically necessary for any kind of AI-related development right now since the field itself is very young and tends to evolve at a rapid pace.
Ubuntu may be one of the most popular Linux distributions out there, and it is also used for AI development purposes on a regular basis. Its overall stability is the main reason it is so well-known – combined with the fact that it supports a variety of ML-oriented libraries and frameworks (PyTorch, SciPy, TensorFlow, NumPy, etc.). The sheer size of Ubuntu’s developer community makes sure that the distribution in question gets both regular updates and a helpful community for solving various issues if they appear.
Debian is another excellent example of a reliable Linux distribution – albeit it is not as popular as Ubuntu. The main focus of Debian is to support various server environments. It works well with solutions like Keras and TensorFlow, and there is even support for a number of AI-related libraries, such as SciPy and NumPy.
CentOS is a good example of a moderately famous Linux distributive that can also be a good place for various AI development tasks. It is fast, stable, reliable, and relatively popular – something that translates directly into the amount of community support an average user can receive to solve some kind of issue. CentOS supports Keras, TensorFlow, and a number of libraries necessary for various Machine Learning tasks and mission-critical applications.
Since applications such as TensorFlow and Keras have been mentioned multiple times already, it would be fair to explain what these solutions are in the first place.
Keras is an API for high-level neural networks commonly used for various forms of AI development. It is written in Python and has a reputation for being simple and flexible. It can be integrated with all kinds of hardware, if necessary, including mobile devices, CPUs, embedded systems, GPUs, and so on.
PyTorch is a well-known ML framework that has plenty of uses in the context of AI development. Its capability to create dynamic computational graphs is one of its most vital points, along with general flexibility and simplicity. It also supports a variety of hardware, including personal computers, mobile devices, embedded systems, etc.
TensorFlow is an exemplary ML framework developed internally by Google employees (a team called Google Brain Team is responsible for it). It is one of the most popular frameworks in this field, offering speed, versatility, and many supported hardware types – GPUs, CPUs, mobile devices, etc.
Of course, these solutions are just the “tip of the iceberg” regarding AI development as a whole. There is an entire market of solutions and platforms that are used in AI development in some way, shape, or form. Docker is an excellent example of such a solution, offering a completely open-source platform for container creation. These containers are used to run various applications in isolated environments, making them a perfect choice for AI app testing on a regular basis.
Lazy AI is another example of a great AI development solution, it is a no-code application development platform that uses templates and the power of a versatile AI engine to create complete applications with no coding involved whatsoever. The process is often referred to as Lazy app development since it is not particularly complicated and usually consists of choosing a specific template for the task and filling in one or several parameters to set everything up.
AI development is a growing field, and its expansion rates are outstanding. There is no shortage of different platforms and operating systems that can be used for AI development, including a variety of Linux distributives that support many ML frameworks and other applications in a similar fashion.
