AI and Machine Learning in Network Operations

A quick test that survives exam pressure: if the tool writes or builds something, think generative; if it forecasts or classifies, think predictive. Do not treat the two terms as interchangeable — sorting an example into the right bucket is exactly what this objective asks for.

Machine learning is the technique underneath. It is software that improves from data over time instead of following only fixed, hand-written rules. Feed it enough examples of normal network behaviour and it learns a baseline; new data departing from that baseline gets flagged. ML powers most predictive AI and much of modern generative AI — so AI is the capability and ML is a common way to build it.

• Baseline normal behaviour — typical latency, interface load, error rates — so that "normal" is measured rather than guessed.
• Detect anomalies — surface the one interface behaving unlike its baseline, instead of making an operator scan hundreds of counters.
• Predict problems — warn of a link likely to congest or a device trending toward failure before users feel it.
• Assist troubleshooting and change — summarise an incident, or suggest a configuration a human then reviews.

• Generative AI creates new content — a config draft, an incident summary.
• Predictive AI forecasts an outcome or value from data — a likely failure, a traffic spike, an anomaly.
• Machine learning learns patterns from data over time and underpins both.
• AIOps applies these to operations: baseline normal, detect anomalies, predict problems, assist change.
• Humans still verify. AI assists; show-command validation does not go away.