--- title: Google Cloud ML description: >- A managed service for easily building machine learning models for data of any type or size. breadcrumbs: Docs > Integrations > Google Cloud ML --- # Google Cloud ML ## Overview{% #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{% #setup %} ### Installation{% #installation %} If you haven't already, set up the [Google Cloud Platform integration first](https://docs.datadoghq.com/integrations/google-cloud-platform.md). There are no other installation steps that need to be performed. ### Log collection{% #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](https://docs.datadoghq.com/integrations/google-cloud-platform.md#log-collection). Once this is done, export your Google Cloud Machine Learning logs from Google Cloud Logging to the Pub/Sub topic: 1. Go to the [Google Cloud Logging page](https://console.cloud.google.com/logs/viewer) and filter the Google Cloud Machine Learning logs. 1. Click **Create Export** and name the sink. 1. 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. 1. Click **Create** and wait for the confirmation message to show up. ## Data Collected{% #data-collected %} ### Metrics{% #metrics %} | | | | | **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* | ### Events{% #events %} The Google Cloud Machine Learning integration does not include any events. ### Service Checks{% #service-checks %} The Google Cloud Machine Learning integration does not include any service checks. ## Troubleshooting{% #troubleshooting %} Need help? Contact [Datadog support](https://docs.datadoghq.com/help/). ## Further Reading{% #further-reading %} - [Machine learning model monitoring: Best practices](https://www.datadoghq.com/blog/ml-model-monitoring-in-production-best-practices/)