Using raw network telemetry traces to generate predictive insights using machine learning

An apparatus, comprising:

• one or more network interfaces;
• a processor coupled to the network interfaces and configured to execute one or more processes;
• and
• a memory configured to store a process executable by the processor, the process when executed configured to:
• receive telemetry data collected from a plurality of different networks;
• combine the telemetry data into a synthetic input trace;
• input the synthetic input trace into a plurality of machine learning models to generate a plurality of predicted key performance indicators (KPIs), each of the models having been trained to assess telemetry data from an associated network in the plurality of different networks and predict a KPI for that network;
• and
• compare the plurality of predicted KPIs to identify one of the plurality of different networks as exhibiting an abnormal behavior.

A tangible, non-transitory, computer-readable medium storing program instructions that cause a service to execute a process comprising:

• receiving, at the service, telemetry data collected from a plurality of different networks;
• combining, by the service, the telemetry data into a synthetic input trace;
• inputting, by the service, the synthetic input trace into a plurality of machine learning models to generate a plurality of predicted key performance indicators (KPIs), each of the models having been trained to assess telemetry data from an associated network in the plurality of different networks and predict a KPI for that network;
• and
• comparing, by the service, the plurality of predicted KPIs to identify one of the plurality of different networks as exhibiting an abnormal behavior.