“What we’ve done is basically take that Llama 3 model, and we’ve now trained it with an enormous corpus of networking telemetry and insights, and really the domain experience that we have at the company,” Kamel explained.
This network-specific language model allows Selector’s platform to understand network terminology and concepts with a high-degree of precision. Kamel said that the model understands network connectivity and all that it entails, including interface descriptions. Users can ask questions about a specific interface an organization is using, for example.
The network language model’s capabilities also extend beyond natural language queries and can tackle complex challenges faced by network teams. For example, Kamel said that Selector is working with a large data center provider that receives information on maintenance windows from network peers around the world. The maintenance window information was coming in different languages, including Japanese, Hindi and French, that weren’t always easily understood.
“What we’re doing with the NLM is we’re basically able to read all of those maintenance windows in real time and then actually use those in order to suppress alerts and then inform people in advance of when certain links will be down globally,” he said.
Unlocking the Power of Digital Twins
Alongside the network language model, Selector AI has introduced a digital twin capability, allowing customers to create a virtual representation of their network.
Kamel said that Selector is able to model an organization’s network, using low-level telemetry in real time and building an in-memory model of what routing and traffic looks like across the entire network.
