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 |
Events
The Google Cloud Machine Learning integration does not include any events.
Service Checks
The Google Cloud Machine Learning integration does not include any service checks.
Troubleshooting
Need help? Contact Datadog support.
