The Relationship Between Artificial Intelligence and Machine Learning in Network Monitoring

Artificial Intelligence (AI) is a field of computer science that emphasizes the creation of intelligent machines that work and react like humans. According to SAS, Artificial Intelligence allows machines to learn from experience, adapt to new inputs, and perform tasks like humans. AI is an important part of the technology industry. What are some examples of tools made from artificial intelligence? An example that is already well known to many people is Siri from Apple Inc. which can respond and recognize your voice just by giving voice commands.

Machine Learning (ML) according to SAS is a data analysis method that automates the creation of analytical models. Machine Learning is a branch of Artificial Intelligence based on the idea that systems can learn from data, identify patterns, and make decisions with manual human intervention. With Machine Learning, computers can handle new situations through self-training, experience, analysis, and observation.

So what is the relationship between Artificial Intelligence and Machine Learning? The relationship between the two is very close. If likened, AI is the umbrella that covers Machine Learning. As explained in the previous paragraph, Machine Learning is a branch or part of AI. The task of Machine Learning is to train machines to learn.

Artificial Intelligence and Machine Learning both play an important role in technological development. In fact, many companies in various fields have now started implementing it. One of them is network monitoring.

Artificial Intelligence and Machine Learning in Network Monitoring

According to Daniel Hein, every day a network can produce a ton of data. To process it, network monitoring requires a system that can help it. Then to process the data and to understand what is happening in the network, network performance monitoring (NPM) as stated above, requires a system to do all the work. The most common use of AI in network monitoring is to process data. Not only that, AI can analyze the data in real-time and can also provide insight into what information is sent and received by your network.

Meanwhile, Machine Learning is used to study historical data to find network trends. What this means is that if a problem arises and is handled, the problematic data will be examined by Machine Learning to get an idea of ​​the problems that occur on the network. This allows Network Performance Monitoring to recognize dangerous data that occurs in the future instantly. Thus, NPM reminds you of potentially dangerous information without doing in-depth analysis every time.

Using Artificial Intelligence and Machine Learning, common issues affecting your network can be easily learned by network performance monitoring. This is because AI can be relied on to apply solutions to common problems without human input. For example, when AI detects the same problem repeatedly, it will find the best solution for the problem. AI can even make decisions on its own when it has enough data to do so.

While Artificial Intelligence and Machine Learning can do their jobs well, they don’t just work on their own. They need to be trained to respond to what is happening on your network. This means that you can customize AI to be able to analyze and respond to specific types of events that may occur on your network.

References:

https://www.sas.com/en_us/insights/analytics/what-is-artificial-intelligence.html

https://www.sas.com/en_id/insights/analytics/machine-learning.html

https://www.techopedia.com/definition/190/artificial-intelligence-ai

The Benefits of AI and Machine Learning in Network Monitoring