What type of logs would indicate an error occurring while an ML skill is live?

What type of logs would indicate an error occurring while an ML skill is live?

• A. ML Skill Activity logs
• B. ML Skill Prediction Event logs
• C. Streaming logs
• D. ML Pipeline logs

Answer: (B)

ML Skill Prediction Event logs are highly relevant for understanding errors that occur while an ML skill is live. These logs capture detailed information about each prediction request made to the ML skill, including successes and failures. When an error happens—such as issues with input data, processing failures, or network problems—these logs will typically log the pertinent error messages or codes associated with the prediction attempt. This makes them an essential resource for diagnosing problems in real-time operation. In contrast, other log types serve different purposes. For instance, ML Skill Activity logs provide insights into the overall usage patterns and performance metrics of the ML skill but may not capture specific errors during predictions. Streaming logs usually focus on the flow of data and actions taken, rather than specific prediction errors. ML Pipeline logs track the processes involved in training or deploying models but do not specifically address live prediction errors. Thus, ML Skill Prediction Event logs are the most appropriate for monitoring and diagnosing live errors in ML skills.

ML Skill Prediction Event logs are highly relevant for understanding errors that occur while an ML skill is live. These logs capture detailed information about each prediction request made to the ML skill, including successes and failures. When an error happens—such as issues with input data, processing failures, or network problems—these logs will typically log the pertinent error messages or codes associated with the prediction attempt. This makes them an essential resource for diagnosing problems in real-time operation.

In contrast, other log types serve different purposes. For instance, ML Skill Activity logs provide insights into the overall usage patterns and performance metrics of the ML skill but may not capture specific errors during predictions. Streaming logs usually focus on the flow of data and actions taken, rather than specific prediction errors. ML Pipeline logs track the processes involved in training or deploying models but do not specifically address live prediction errors. Thus, ML Skill Prediction Event logs are the most appropriate for monitoring and diagnosing live errors in ML skills.