Google Machine Learning

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Overview

Google Cloud Machine Learning is a managed service that enables you to easily build machine learning models, that work on any type of data, of any size.

Get metrics from Google Machine Learning to:

Visualize the performance of your ML Services.
Correlate the performance of your ML Services with your applications.

Setup

Installation

If you haven’t already, set up the Google Cloud Platform integration first. There are no other installation steps that need to be performed.

Log collection

Google Cloud Machine Learning logs are collected with Google Cloud Logging and sent to a Dataflow job through a Cloud Pub/Sub topic. If you haven’t already, set up logging with the Datadog Dataflow template.

Once this is done, export your Google Cloud Machine Learning logs from Google Cloud Logging to the Pub/Sub topic:

Go to the Google Cloud Logging page and filter the Google Cloud Machine Learning logs.
Click Create Export and name the sink.
Choose “Cloud Pub/Sub” as the destination and select the Pub/Sub topic that was created for that purpose. Note: The Pub/Sub topic can be located in a different project.
Click Create and wait for the confirmation message to show up.

Data Collected

Metrics

gcp.ml.error_count (count)	Cumulative count of prediction errors. Shown as error
gcp.ml.latency (gauge)	Latency of a certain type. Shown as millisecond
gcp.ml.prediction_count (count)	Cumulative count of predictions. Shown as prediction
gcp.ml.response_count (count)	Cumulative count of different response codes. Shown as response
gcp.ml.cpu_utilization (gauge)	Fraction of the allocated CPU that is currently in use. Shown as percent
gcp.ml.memory_utilization (gauge)	Fraction of the allocated memory that is currently in use. Shown as percent