9/27/2019 10:28 AM
There are aspects of Artificial Intelligence (Machine Learning - Like bayeasin algorithms used in email spam filtering) and actual intelligent actions (AI - where the systems capture information, usually from machine learning algorithms, and takes actions independently). All of these tools are useful based on how well the system learns information about the network systems that they monitor. That process is called supervised learning. Studies from IBM have determined that AI's are best when they are combined with human interaction, otherwise known as hybrid AI.
Network performance monitors (NPMs) can use AI to examine and respond to performance data. AI can sift through large amounts of data as the data travels across the network. AI can analyze that data in real time, giving insight into the information your network sends and receives and sending out alerts. As an AI learns more about the network, the AI can begin to suggest or implement solutions. For example, if the AI detects an issue on the network that happens continuously, the AI can figure out which solutions will be the best to deal with the issue. Eventually, the AI will have collected enough data on the network to be able to make decisions on its own if you choose. AI and machine learning don’t just work on their own. They need to be trained to respond to events that occur on the network. AI can be customized to analyze and respond to specific events. If there is an issue you want the AI to solve on its own, you can train it to take certain actions when events happen. AI allows engineers to spend more time on critical activities and lets the AI detect and monitor the network. AI and machine learning allow you to see what is happening on your network in real time and can vastly improve your network monitoring experience.
The best AI's allow the end user to influence and control the supervised learning aspects of the NPM, so that the AIs responses can be managed and monitored by the end user.
This year at Cisco Live, Cisco announced limited integration of AI into their solutions. They debuted a series of software enhancements designed to put AI and machine learning deeper into their networks. Key features include new network automation and analytics tools that will help IT teams gain more insights and visibility from network data. Other solutions such as Splunk have predictive analytics based on machine learning algorithms.
Vigilant's Intelligent Workspace and Instant Einstein bots enable AI integration with existing technology and monitoring solutions to only a true Hybrid AI solution that is technology independent using Simple Network Management Protocol (SNMP) and technology adapters.
What solution is best depends on the current technologies in place and the desired level of human interactions.
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