Determining server error types

A non-transitory computer-readable storage medium storing instructions, the instructions comprising:

• a plurality of instructions that, when executed by one or more processors, cause the one or more processors to:
• compile a plurality of server logs, from one or more servers, including a server log associated with two server error types of a plurality of server error types,
• wherein the two server error types include:
• a hardware error, and
• a software error,
• identify, based on the compiling, one or more server errors associated with one or more error types of the plurality of server error types,
• determine, based on processing of a historical record associated with the one or more server errors, a respective classification score for each server error of the one or more servers,
• determine, based on using a machine learning model and processing of the historical record and the respective classification score, a respective server error type, of the plurality of error types, for each server error of the one or more server errors,
• wherein the machine learning model is trained based on performing dimensionality reduction to reduce historical structure data to a feature set, and
• cause, based on the respective server error type, the one or more servers to perform an action concerning the respective server error type.

A method, comprising:

• compiling, by a device, a plurality of server logs from one or more servers, including a server log associated with two server error types of a plurality of server error types,
• wherein the two server error types include:
• a hardware error, and
• a software error;
• identifying, by the device and based on the compiling, one or more server errors associated with one or more error types of the plurality of server error types;
• determining, by the device and based on processing of a historical record associated with the one or more server errors and determined to have a particular classification score, a respective classification score for each of the one or more server errors;
• determining, by the device and based on using a machine learning model and based on the respective classification score, a respective server error type, of the plurality of server error types, for each server error of the one or more server errors,
• wherein the machine learning model is trained based on performing dimensionality reduction to reduce historical structure data to a feature set;
• and
• causing, by the device and based on the respective server error type, the one or more servers to perform an action concerning the respective server error type.