Using AI/ML in Network Monitoring

Data quality refers to whether the information provided by the sensors is accurate enough to provide meaningful insights.

AIML are becoming more prevalent across the network, from edge to core. They’re being used for everything from security threat detection to traffic analysis. But what does this mean for your organization? How can you leverage these technologies effectively? And how do they affect existing processes and procedures?

However, as time goes on, we will see more and more networks being monitored using AI and ML techniques. Here are some reasons why you should consider adding an AI or ML system to your existing network monitoring tool:

  1. It can make better decisions than humans.
  2. It doesn’t get tired like human operators do.
  3. It learns over time.
  4. It does not require any manual intervention.
  5. It provides instant alerts when something happens.
  6. It reduces false positives.
  7. It helps reduce costs by automating repetitive tasks.
  8. It improves efficiency.
  9. It increases productivity.
  10. It makes sure everything runs smoothly.

It matters because it affects the way we operate our business. In fact, according to Gartner, “By 2022, 80% of enterprises will rely heavily on AI-powered analytics to drive operational efficiency.” As mentioned earlier, AI helps reduce human error while automating tedious tasks. So, not only does it save money, it makes people happier!

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ravien

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