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What OS is best for Data Science and Machine Learning? Linux or Windows?

Monitor, Informatics, Windows, Microsoft, Apple, Linux

So, you are a fresh data scientist and dipping your toes on machine learning libraries like tensor flow or PyTorch. You are full of enthusiasm, about to set your first environment, then you stop, wondering: Which OS do you use? Windows or Linux? You ask your colleagues, and the opinions are divided and heated. 

Choosing the right OS for your project can be difficult. From youth, we are all wired to think that you are a Windows or Mac person. When you start on advanced software, data science, artificial intelligence, or machine learning, you find most professionals work with one of two options: Windows or Linux.  

So, which one should you choose? Hopefully, this post can save you time and help you understand what OS is the best for your project. 

Linux for machine learning overview

Linux is an open-source, kernel-distributed operating system. The kernel is the program at the center of the Linux operating system that manages critical operations, like communicating with the hardware. 

Why choose Linux for data science and machine learning?

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A broad and engaged community 

Because Linux is an open-source operating system, it has a large community of contributors. The Linux community is very involved, and there is a wide network of support if you need to search for an error. There is no shortage of libraries and tutorials. On dagshub.com you can find an excellent introduction to Linux for data science. 

Faster computers

Linux offers more computing power than Windows. That’s why 90% of the world’s supercomputers run on Linux. Running Linux gives data scientists the speed they need to run large amounts of data. 

Another advantage is that by using Linux, you can use NVIDIA Docker. 

Flexibility

The Linux OS offers several software choices for doing the same task. Linux has more usability and features than Windows. One of the key characteristics of Linux is its flexibility of functionality. It consumes few resources to run, and it can be run on older hardware. 

Free applications

This advantage cannot be overlooked. Linux OS is free. So if you are a data scientist and a fan of open-source projects, you can contribute to the Linux community by improving applications according to your data science needs. You can even modify the source code and add more features. 

Security

Linux has a robust nature, thus is relatively more secure than Windows. The CLI command line offers extra security over Windows because it is not easy to install an app or executable. The terminal is straightforward to use not only for scripting but for Linux commands. 

Performance

Users say Linux outperforms Windows in data transfer command as well as per application installs. It works faster, even for advanced artificial intelligence and Deep Learning. 

Modularity

It is easier to manage Linux since you’ve got Super User privileges with just a command. Additionally, partitioning is much easier in Linux. 

There are, however, some disadvantages of Linux OS. There is not a single way for packaging software, no standard desktop environment. Additionally, it doesn’t support games well. 

Windows for machine learning overview

Windows ML is the Microsoft API for machine learning. It focuses on deploying hardware-accelerated inferences on Windows devices. It works on the latest versions of Windows 10 and Windows Server.

Why choose Windows for machine learning?

Hardware support

Windows ML enables you to write your ML workload, and it will automatically get an optimized performance across hardware and device types, such as GPUs, CPUs, and accelerators. 

Low latency

You can evaluate ML models with Windows in real-time, allowing the analysis of large data volumes. It performs well on game engines or background tasks. 

Flexibility

Windows gives you the option to evaluate ML models locally. This feature provides you the flexibility to try different scenarios. For instance, having the models run when the device is offline. 

Reduced operational cost

You can try the models in the cloud and then analyze them on Windows devices, saving bandwidth costs. You can save even more costs by using Windows hardware acceleration for model serving, which reduces the number of machines you need. 

There are downsides to using Windows for data science and machine learning. The cost would be the first one. Windows is not free or open-sourced. The performance it offers isn’t as good, maybe that is why only 1% of supercomputers run on Windows. 

Linux vs. Windows? Which is best? 

While it will depend on your specific case need, most data scientists prefer Linux over Windows. It is more user-friendly, flexible, and equipped to deal with the amount of data of a machine learning project without breaking the bank.

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