Google Machine Learning

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개요

Google Cloud 기계 학습은 모든 유형의 데이터에서 규모와 상관없이 동작하는 기계 학습 모델을 쉽게 구축할 수 있도록 도와드리는 관리형 서비스입니다.

Google 기계 학습 메트릭을 수집하면 다음을 할 수 있습니다.

ML 서비스 성능을 시각화합니다.
ML 서비스의 성능과 애플리케이션의 상관 관계를 파악합니다.

설정

설치

아직 설정하지 않았다면, 먼저 Google Cloud Platform 통합을 설정하세요. 그 외 다른 설치 단계는 없습니다.

로그 수집

Google Cloud 기계 학습 로그는 Google Cloud Logging으로 수집하여 클라우드 Pub/Sub 토픽을 통해 데이터 플로우 작업으로 전송됩니다. 아직 설정하지 않았다면 Datadog 데이터 플로우 템플릿으로 로깅을 설정하세요.

해당 작업이 완료되면 Google Cloud Logging에서 Google Cloud 기계 학습 로그를 다음 Pub/Sub 주제로 내보냅니다.

Google Cloud Logging 페이지로 이동해 Google Cloud 기계 학습 로그를 필터링하세요.
Create Export를 클릭하고 싱크 이름을 지정하세요.
“Cloud Pub/Sub"를 대상으로 선택하고 해당 목적으로 생성된 Pub/Sub 주제를 선택합니다. 참고: Pub/Sub 주제는 다른 프로젝트에 있을 수 있습니다.
Create를 클릭하고 확인 메시지가 나타날 때까지 기다립니다.

수집한 데이터

메트릭

gcp.ml.prediction.error_count (count)	Cumulative count of prediction errors.
gcp.ml.prediction.latencies.avg (count)	The average latency of a certain type. Shown as microsecond
gcp.ml.prediction.latencies.samplecount (count)	The sample count for latency of a certain type. Shown as microsecond
gcp.ml.prediction.latencies.sumsqdev (count)	The sum of squared deviation for latency of a certain type. Shown as microsecond
gcp.ml.prediction.online.accelerator.duty_cycle (gauge)	Average fraction of time over the past sample period during which the accelerator(s) were actively processing.
gcp.ml.prediction.online.accelerator.memory.bytes_used (gauge)	Amount of accelerator memory allocated by the model replica. Shown as byte
gcp.ml.prediction.online.cpu.utilization (gauge)	Fraction of CPU allocated by the model replica and currently in use. May exceed 100% if the machine type has multiple CPUs.
gcp.ml.prediction.online.memory.bytes_used (gauge)	Amount of memory allocated by the model replica and currently in use. Shown as byte
gcp.ml.prediction.online.network.bytes_received (count)	Number of bytes received over the network by the model replica. Shown as byte
gcp.ml.prediction.online.network.bytes_sent (count)	Number of bytes sent over the network by the model replica. Shown as byte
gcp.ml.prediction.online.replicas (gauge)	Number of active model replicas.
gcp.ml.prediction.online.target_replicas (gauge)	Aspired number of active model replicas.
gcp.ml.prediction.prediction_count (count)	Cumulative count of predictions.
gcp.ml.prediction.response_count (count)	Cumulative count of different response codes.
gcp.ml.training.accelerator.memory.utilization (gauge)	Fraction of allocated accelerator memory that is currently in use. Values are numbers between 0.0 and 1.0, charts display the values as a percentage between 0% and 100%.
gcp.ml.training.accelerator.utilization (gauge)	Fraction of allocated accelerator that is currently in use. Values are numbers between 0.0 and 1.0, charts display the values as a percentage between 0% and 100%.
gcp.ml.training.cpu.utilization (gauge)	Fraction of allocated CPU that is currently in use. Values are numbers between 0.0 and 1.0, charts display the values as a percentage between 0% and 100%.
gcp.ml.training.memory.utilization (gauge)	Fraction of allocated memory that is currently in use. Values are numbers between 0.0 and 1.0, charts display the values as a percentage between 0% and 100%.
gcp.ml.training.network.received_bytes_count (count)	Number of bytes received by the training job over the network. Shown as byte
gcp.ml.training.network.sent_bytes_count (count)	Number of bytes sent by the training job over the network. Shown as byte