I’m sure you’ve heard the terms machine learning and artificial intelligence within the networking industry more than you can count in the past year. And although it is tempting to get excited about new tools that can increase our productivity, it can be difficult to see the true value of these technologies.

Both technologies are promising to transform our operations by lowering costs and giving us critical insights into the user experience. This alone is set to improve our productivity more than anything else has in the past. However, there is an apprehension felt about machine learning and AI, some individuals believing it will do more harm than good.

What do you think? Let’s discuss the possible impact of machine learning on network management.

Problems to Overcome in Network Management

First things first, it’s important to understand the problems we need to overcome within network management to understand how machine learning and AI can make a difference.

As network managers, we work day in and day out attempting to sift through incredible amounts of data from server logs, packets and controllers. We then must analyze that data and determine if the network is healthy or in need of an adjustment. Finally, we must manually apply changes to the network to solve problems with connectivity and security. Do we know whether or not the changes worked? Not all the time.

It takes time and effort to go through copious amounts of data on a daily basis. Sometimes, smaller organizations and businesses do not have the manpower to make it happen. Fortunately for us, this is where machine learning and AI come in as solutions to these problems.

What is Machine Learning and AI?

According to SAS, machine learning is “a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify problems and make decisions with minimal human intervention.”

Machine learning, put simply, is the idea that machines can learn and make decisions based on prior algorithms and equations. For example, the self-driving Google car or Netflix movie recommendations. It is the idea that machines can perform functions on their own, without a human doing it for them.

SAS also states that artificial intelligence “makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data.

Artificial intelligence is all about the automating of specific computerized tasks. Differing from machine learning, AI requires human involvement to set up the system and ask the right questions. For example, Apple’s Siri and website chatbots. Some AI tools act like human entities while others are simply robots that are linked to simple and complex tasks.

The Impact on Network Management

So, how can machine learning and its counterpart AI be applied to network management to make a difference? How do the two drive-down costs while increasing our productivity? Machine learning can ultimately automate many of the processes network managers must do manually.

For example, machine learning algorithms can predict potential network problems prior to them happening. They can pinpoint capacity requirements early. These algorithms can also identify user network problems and make recommendations to fix them. Machine learning takes the questioning out of the network management game and instead allows managers to get to the bottom of network issues fast, without pointing fingers.

The best part? Machine learning uses the data that is already running throughout your network, without the need to use extra servers or software. ML requires large amounts of data to be fed through its system in order to work properly. In some industries, this is incredibly difficult. Within network management, data is already abundant, making it easy for a machine learning solution to be utilized daily.

Network World recently published an article including questions that machine learning can help network managers answer. Some of these questions include:

• Are issues occurring on a specific VLAN? If so, is there a specific location?
• Is the problem with a certain Wi-Fi access point of group of APs?
• Is the problem an application problem?
• What are some concrete actions I can take to improve DNS experience in my network?

As a network manager, being able to pinpoint problems that fast would change the way you work and use your time on a daily basis. You would be able to start fixing network issues before they became too large and brainstorm on ways to improve the network for the future.

Machine learning and AI is set to make the network world a better place for us all. From identifying potential network issues to answering critical network questions, machine learning is the next best thing.