5 ways in which unikernels can influence machine learning 

Machine learning is a subset of artificial intelligence – the technology that involves computer science and artificial technology discovering and imitating the ways that humans learn. By analyzing human behavior, seeing our patterns, and figuring out what makes us run, machine learning can improve the accuracy of its systems and see how to mimic humans in the most lifelike way possible.

For example, one company that is familiar and well-versed in machine learning is the computer giant, IBM. By creating supercomputers and machine learning applications that mimic human behavior, IBM was able to play chess against computers in the 1960s, speak with computers, and create more advanced technologies, like self-driving cars.

But how can an operating system influence machine learning? Let’s talk about one of the most popular types of operating systems today – unikernels. Unikernels are a single space machine image that works together with a single operating system; it helps efficiently and securely run applications. But how does this new technology influence machine learning and artificial intelligence? Find out here!

5 ways in which unikernels can influence machine learning

Issues regarding 3rd party libraries

Although unikernels are effective in many business processes, such as running applications super quickly and fighting off hackers, they can pose some challenges. In terms of machine learning, it has been found through studies that unikernels face current concerns regarding third-party compatibility with libraries. In this case, businesses and tech companies will have to use another method that helps safely share images.

Security concerns

Even though unikernels have a smaller surface area and are more secure than the alternatives – like containers and virtual machines – there are still currently concerns that it is not the best choice yet for machine learning. When compared with another solution that uses a Linux operating system, unikernels have a few drawbacks. Before they can be completely utilized as the sole method with machine learning, unikernels need to fit any security concerns that result in duplication of code that can slow down processing times.

Benefits of web tasks

When it comes to machine learning, unikernels stand out among the other options for web-related processes. Since their initial installation is so quick, and boots in just seconds, it is the fastest and most effective method for running applications from the web. With the efficiency of being able to execute within seconds, this means it is easier to start in terms of using a machine learning interface.

Increased security

The increased security due to the lower surface area of unikernels can make a great argument that this is the best option for machine learning. There have been studies that show up to a 90% reduction in total bytes of code, translating it to the same percentage of a security benefit compared to containers or virtual machines.

Horizontal scaling

The last aspect to take into consideration with earnest and machine learning is the ability to horizontally scale applications. Since unikernels can scale with ease, this means that machine learning can use other applications at the same time.


Even though unikernels may need development in the upcoming years – in terms of security for machine learning computers – there have been numerous benefits that show it can be the best option for machine learning and artificial intelligence such as horizontal scaling and fast processing times.



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