System and method for identifying anomalous network devices or services using gradient boosting and large language models

A method for identifying anomalous network devices or services using gradient boosting decision trees coupled with large language models, comprising:

• monitoring performance of a cloud computing or cloud infrastructure environment comprising a plurality of network devices or services operating therein;
• in response to detecting a reduced performance metric or performance anomaly in a cloud computing environment,
• gathering health metrics of network devices, and
• analyzing the health metrics using gradient boosting to identify potentially-unhealthy devices or services;
• and
• subsequent to identifying the potentially-unhealthy devices or services,
• obtaining ticket information associated with the potentially-unhealthy devices or services during a time period prior to the determination of the reduced performance metric or performance anomaly, and generate a context for use with a large language model, and
• generating a report, by reference to a large language model, of a subset of the potentially-unhealthy devices or services that are likely associated with the reduced performance metric or performance anomaly.

A non-transitory computer readable storage medium, including instructions stored thereon which when read and executed by one or more computers cause the one or more computers to perform a method comprising:

• monitoring performance of a cloud computing or cloud infrastructure environment comprising a plurality of network devices or services operating therein;
• in response to detecting a reduced performance metric or performance anomaly in a cloud computing environment,
• gathering health metrics of network devices, and
• analyzing the health metrics using gradient boosting to identify potentially-unhealthy devices or services;
• and
• subsequent to identifying the potentially-unhealthy devices or services,
• obtaining ticket information associated with the potentially-unhealthy devices or services during a time period prior to the determination of the reduced performance metric or performance anomaly, and generate a context for use with a large language model, and
• generating a report, by reference to a large language model, of a subset of the potentially-unhealthy devices or services that are likely associated with the reduced performance metric or performance anomaly.