As more enterprises move data and applications out of the data center into hybrid or multicloud platforms, networks—and network operations—have become increasingly complicated. On-premises architecture was never exactly simple, but today IT must manage the data center as well as many other connected environments, including public and private clouds and do so in real-time.
The technology that was developed to improve IT and aid digital transformation may have enabled enterprises to increase agility, improve scalability, and enhance application development, but it has also added much more complexity from an operational point of view.
For network operations teams, keeping the network up and running is a top priority. Monitoring networks to quickly identify issues and get them resolved is a natural imperative—but with more complex networks come more complex and difficult troubleshooting.
Today, AI technology is increasingly perceived by many enterprises and industry experts as the Next Great Thing that will transform network operations. AI has great potential in improving NetOps, certainly. But what many in the industry don’t realize, however, is that NetOps requires real-time AI. Because most generic AI tools are not real-time, they are a poor fit for applying AIOps to networks.
Not All AI Solutions Are Real-Time
In network operations, speed and efficiency is critical to ensuring that an enterprise’s network is always available. When issues arise, the faster they are identified, the faster they can be fixed—often before customers even realize there was a problem.
AI solutions in the broader technology landscape are typically based on historical data—which means a data science team had to take data from the last several weeks, months, or even years and train algorithms on that data to come up with a model.
But here’s the catch. Those models are created basically offline with old data, and are updated periodically, often weekly or monthly. However, networks are changing all the time and models need to adapt to these changes. As a result, offline models based on old data are not well-suited for networks and result in significant false positives or false negatives.
What if your platform’s AI models could be updated every few seconds or minutes, virtually in real time? What if there was a solution that continually relearned from real-time data as traffic, metrics, flows, log patterns, and more changed?
Real-time AI, which uses what the industry calls “online learning,” could change the game when it comes to smooth, efficient network operations.
Want to learn more? Check out the Packet Pushers podcast, where Augtera founder and CEO Rahul Aggarwal discusses real-time Network AI.
Augtera Network AI is Real-Time
One of Augtera’s key differentiation is that its purpose built for real-time AI at scale. Not only is it designed from the ground up for network operations but also the real-time AI capabilities allow Augtera AI to address use cases in the broader IT infrastructure, network security and application eco-system.
The platform cost-effectively builds millions of models across hundreds of metrics and continuously updates these models in real-time. It builds models across Big Data comprising logs and incrementally updates these models from the hundreds of millions to billions of logs as they come streaming in. Predictions are made in real-time based on these models. Anomalies and events are automatically correlated using network topology also in real-time.
The platform delivers operationally relevant and actionable AI insights in real-time as notifications and tickets with minimal false positives.
Augtera enables a whole new level of capabilities for network operations teams. With real-time, online learning, the platform not only helps IT act quicker than ever to address network issues, but it also can identify more relevant issues and events and shut out the noise from events such as planned maintenance.
To see for yourself how you can improve network operations with Augtera real-time AI please visit our real-time demo lab.
