--- title: Amazon SageMaker description: Amazon SageMaker is a fully managed machine learning service. breadcrumbs: Docs > Integrations > Amazon SageMaker --- # Amazon SageMaker ## Overview{% #overview %} Amazon SageMaker is a fully managed machine learning service. With Amazon SageMaker, data scientists and developers can build and train machine learning models, and then directly deploy them into a production-ready hosted environment. Enable this integration to see all your SageMaker metrics in Datadog. ## Setup{% #setup %} ### Installation{% #installation %} If you haven't already, set up the [Amazon Web Services integration](https://docs.datadoghq.com/integrations/amazon_web_services/) first. ### Metric collection{% #metric-collection %} 1. In the [AWS integration page](https://app.datadoghq.com/integrations/amazon-web-services), ensure that `SageMaker` is enabled under the `Metric Collection` tab. 1. Install the [Datadog - Amazon SageMaker integration](https://app.datadoghq.com/integrations/amazon-sagemaker). ### Log collection{% #log-collection %} #### Enable logging{% #enable-logging %} Configure Amazon SageMaker to send logs either to a S3 bucket or to CloudWatch. **Note**: If you log to a S3 bucket, make sure that `amazon_sagemaker` is set as *Target prefix*. #### Send logs to Datadog{% #send-logs-to-datadog %} 1. If you haven't already, set up the [Datadog log collection AWS Lambda function](https://docs.datadoghq.com/integrations/amazon_web_services/?tab=automaticcloudformation#log-collection). 1. Once the lambda function is installed, manually add a trigger on the S3 bucket or CloudWatch log group that contains your Amazon SageMaker logs in the AWS console: - [Add a manual trigger on the S3 bucket](https://docs.datadoghq.com/logs/guide/send-aws-services-logs-with-the-datadog-lambda-function/#collecting-logs-from-s3-buckets) - [Add a manual trigger on the CloudWatch Log Group](https://docs.datadoghq.com/logs/guide/send-aws-services-logs-with-the-datadog-lambda-function/#collecting-logs-from-cloudwatch-log-group) ## Data Collected{% #data-collected %} ### Metrics{% #metrics %} | | | | | **aws.sagemaker.consumed\_read\_requests\_units**(count) | The average number of consumed read units over the specified time period. | | **aws.sagemaker.consumed\_read\_requests\_units.maximum**(count) | The maximum number of consumed read units over the specified time period. | | **aws.sagemaker.consumed\_read\_requests\_units.minimum**(count) | The minimum number of consumed read units over the specified time period. | | **aws.sagemaker.consumed\_read\_requests\_units.p90**(count) | The 90th percentile of consumed read units over the specified time period. | | **aws.sagemaker.consumed\_read\_requests\_units.p95**(count) | The 95th percentile of consumed read units over the specified time period. | | **aws.sagemaker.consumed\_read\_requests\_units.p99**(count) | The 99th percentile of consumed read units over the specified time period. | | **aws.sagemaker.consumed\_read\_requests\_units.samplecount**(count) | The sample count of consumed read units over the specified time period. | | **aws.sagemaker.consumed\_read\_requests\_units.sum**(count) | The sum of consumed read units over the specified time period. | | **aws.sagemaker.consumed\_write\_requests\_units**(count) | The average number of consumed write units over the specified time period. | | **aws.sagemaker.consumed\_write\_requests\_units.maximum**(count) | The maximum number of consumed write units over the specified time period. | | **aws.sagemaker.consumed\_write\_requests\_units.minimum**(count) | The minimum number of consumed write units over the specified time period. | | **aws.sagemaker.consumed\_write\_requests\_units.p90**(count) | The 90th percentile of consumed write units over the specified time period. | | **aws.sagemaker.consumed\_write\_requests\_units.p95**(count) | The 95th percentile of consumed write units over the specified time period. | | **aws.sagemaker.consumed\_write\_requests\_units.p99**(count) | The 99th percentile of consumed write units over the specified time period. | | **aws.sagemaker.consumed\_write\_requests\_units.samplecount**(count) | The sample count of consumed write units over the specified time period. | | **aws.sagemaker.consumed\_write\_requests\_units.sum**(count) | The sum of consumed write units over the specified time period. | | **aws.sagemaker.invocation\_4xxerrors**(count) | The average number of InvokeEndpoint requests where the model returned a 4xx HTTP response code.*Shown as request* | | **aws.sagemaker.invocation\_5xxerrors**(count) | The average number of InvokeEndpoint requests where the model returned a 5xx HTTP response code.*Shown as request* | | **aws.sagemaker.invocation\_model\_errors**(count) | The number of model invocation requests which did not result in 2XX HTTP response. This includes 4XX/5XX status codes, low-level socket errors, malformed HTTP responses, and request timeouts. | | **aws.sagemaker.invocations**(count) | The number of InvokeEndpoint requests sent to a model endpoint.*Shown as request* | | **aws.sagemaker.invocations\_per\_instance**(count) | The number of invocations sent to a model normalized by InstanceCount in each ProductionVariant. | | **aws.sagemaker.model\_cache\_hit**(count) | The number of InvokeEndpoint requests sent to the multi-model endpoint for which the model was already loaded.*Shown as request* | | **aws.sagemaker.model\_cache\_hit.sum**(count) | The sum of InvokeEndpoint requests sent to the multi-model endpoint for which the model was already loaded.*Shown as request* | | **aws.sagemaker.model\_downloading\_time**(gauge) | The interval of time that it takes to download the model from Amazon Simple Storage Service (Amazon S3).*Shown as microsecond* | | **aws.sagemaker.model\_downloading\_time.maximum**(gauge) | The maximum interval of time that it takes to download the model from Amazon Simple Storage Service (Amazon S3).*Shown as microsecond* | | **aws.sagemaker.model\_downloading\_time.minimum**(gauge) | The minimum interval of time that it takes to download the model from Amazon Simple Storage Service (Amazon S3).*Shown as microsecond* | | **aws.sagemaker.model\_downloading\_time.samplecount**(count) | The sample count interval of time that it takes to download the model from Amazon Simple Storage Service (Amazon S3).*Shown as microsecond* | | **aws.sagemaker.model\_downloading\_time.sum**(gauge) | The sum interval of time that it takes to download the model from Amazon Simple Storage Service (Amazon S3).*Shown as microsecond* | | **aws.sagemaker.model\_latency**(gauge) | The average interval of time taken by a model to respond as viewed from Amazon SageMaker.*Shown as microsecond* | | **aws.sagemaker.model\_latency.maximum**(gauge) | The maximum interval of time taken by a model to respond as viewed from Amazon SageMaker.*Shown as microsecond* | | **aws.sagemaker.model\_latency.minimum**(gauge) | The minimum interval of time taken by a model to respond as viewed from Amazon SageMaker.*Shown as microsecond* | | **aws.sagemaker.model\_latency.samplecount**(count) | The sample count interval of time taken by a model to respond as viewed from Amazon SageMaker.*Shown as microsecond* | | **aws.sagemaker.model\_latency.sum**(gauge) | The sum of the interval of time taken by a model to respond as viewed from Amazon SageMaker.*Shown as microsecond* | | **aws.sagemaker.model\_loading\_time**(gauge) | The interval of time that it takes to load the model through the container's LoadModel API call.*Shown as microsecond* | | **aws.sagemaker.model\_loading\_time.maximum**(gauge) | The maximum interval of time that it takes to load the model through the container's LoadModel API call.*Shown as microsecond* | | **aws.sagemaker.model\_loading\_time.minimum**(gauge) | The minimum interval of time that it takes to load the model through the container's LoadModel API call.*Shown as microsecond* | | **aws.sagemaker.model\_loading\_time.samplecount**(count) | The sample count interval of time that it takes to load the model through the container's LoadModel API call.*Shown as microsecond* | | **aws.sagemaker.model\_loading\_time.sum**(gauge) | The sum interval of time that it takes to load the model through the container's LoadModel API call.*Shown as microsecond* | | **aws.sagemaker.model\_loading\_wait\_time**(gauge) | The interval of time that an invocation request has waited for the target model to be downloaded, or loaded, or both in order to perform inference.*Shown as microsecond* | | **aws.sagemaker.model\_loading\_wait\_time.maximum**(gauge) | The maximum interval of time that an invocation request has waited for the target model to be downloaded, or loaded, or both in order to perform inference.*Shown as microsecond* | | **aws.sagemaker.model\_loading\_wait\_time.minimum**(gauge) | The minimum interval of time that an invocation request has waited for the target model to be downloaded, or loaded, or both in order to perform inference.*Shown as microsecond* | | **aws.sagemaker.model\_loading\_wait\_time.samplecount**(count) | The sample count interval of time that an invocation request has waited for the target model to be downloaded, or loaded, or both in order to perform inference.*Shown as microsecond* | | **aws.sagemaker.model\_loading\_wait\_time.sum**(gauge) | The sum interval of time that an invocation request has waited for the target model to be downloaded, or loaded, or both in order to perform inference.*Shown as microsecond* | | **aws.sagemaker.model\_setup\_time**(gauge) | The average time it takes to launch new compute resources for a serverless endpoint.*Shown as microsecond* | | **aws.sagemaker.model\_setup\_time.maximum**(gauge) | The maximum interval of time it takes to launch new compute resources for a serverless endpoint.*Shown as microsecond* | | **aws.sagemaker.model\_setup\_time.minimum**(gauge) | The minimum interval of time it takes to launch new compute resources for a serverless endpoint.*Shown as microsecond* | | **aws.sagemaker.model\_setup\_time.samplecount**(count) | The sample_count of the amount of time it takes to launch new compute resources for a serverless endpoint.*Shown as microsecond* | | **aws.sagemaker.model\_setup\_time.sum**(gauge) | The total amount of time takes to launch new compute resources for a serverless endpoint.*Shown as microsecond* | | **aws.sagemaker.model\_unloading\_time**(gauge) | The interval of time that it takes to unload the model through the container's UnloadModel API call.*Shown as microsecond* | | **aws.sagemaker.model\_unloading\_time.maximum**(gauge) | The maximum interval of time that it takes to unload the model through the container's UnloadModel API call.*Shown as microsecond* | | **aws.sagemaker.model\_unloading\_time.minimum**(gauge) | The minimum interval of time that it takes to unload the model through the container's UnloadModel API call.*Shown as microsecond* | | **aws.sagemaker.model\_unloading\_time.samplecount**(count) | The sample count interval of time that it takes to unload the model through the container's UnloadModel API call.*Shown as microsecond* | | **aws.sagemaker.model\_unloading\_time.sum**(gauge) | The sum interval of time that it takes to unload the model through the container's UnloadModel API call.*Shown as microsecond* | | **aws.sagemaker.overhead\_latency**(gauge) | The average interval of time added to the time taken to respond to a client request by Amazon SageMaker overheads.*Shown as microsecond* | | **aws.sagemaker.overhead\_latency.maximum**(gauge) | The maximum interval of time added to the time taken to respond to a client request by Amazon SageMaker overheads.*Shown as microsecond* | | **aws.sagemaker.overhead\_latency.minimum**(gauge) | The minimum interval of time added to the time taken to respond to a client request by Amazon SageMaker overheads.*Shown as microsecond* | | **aws.sagemaker.overhead\_latency.samplecount**(count) | The sample count of the interval of time added to the time taken to respond to a client request by Amazon SageMaker overheads.*Shown as microsecond* | | **aws.sagemaker.overhead\_latency.sum**(gauge) | The sum of the interval of time added to the time taken to respond to a client request by Amazon SageMaker overheads.*Shown as microsecond* | | **aws.sagemaker.endpoints.cpuutilization**(gauge) | The average percentage of CPU units that are used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.endpoints.cpuutilization.maximum**(gauge) | The maximum percentage of CPU units that are used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.endpoints.cpuutilization.minimum**(gauge) | The minimum percentage of CPU units that are used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.endpoints.disk\_utilization**(gauge) | The average percentage of disk space used by the containers on an instance uses.*Shown as percent* | | **aws.sagemaker.endpoints.disk\_utilization.maximum**(gauge) | The maximum percentage of disk space used by the containers on an instance uses.*Shown as percent* | | **aws.sagemaker.endpoints.disk\_utilization.minimum**(gauge) | The minimum percentage of disk space used by the containers on an instance uses.*Shown as percent* | | **aws.sagemaker.endpoints.gpumemory\_utilization**(gauge) | The average percentage of GPU memory used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.endpoints.gpumemory\_utilization.maximum**(gauge) | The maximum percentage of GPU memory used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.endpoints.gpumemory\_utilization.minimum**(gauge) | The minimum percentage of GPU memory used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.endpoints.gpuutilization**(gauge) | The average percentage of GPU units that are used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.endpoints.gpuutilization.maximum**(gauge) | The maximum percentage of GPU units that are used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.endpoints.gpuutilization.minimum**(gauge) | The minimum percentage of GPU units that are used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.endpoints.loaded\_model\_count**(count) | The number of models loaded in the containers of the multi-model endpoint. This metric is emitted per instance. | | **aws.sagemaker.endpoints.loaded\_model\_count.sum**(count) | The sum of models loaded in the containers of the multi-model endpoint. This metric is emitted per instance. | | **aws.sagemaker.endpoints.memory\_utilization**(gauge) | The average percentage of memory that is used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.endpoints.memory\_utilization.maximum**(gauge) | The maximum percentage of memory that is used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.endpoints.memory\_utilization.minimum**(gauge) | The minimum percentage of memory that is used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.processingjobs.cpuutilization**(gauge) | The average percentage of CPU units that are used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.processingjobs.cpuutilization.maximum**(gauge) | The maximum percentage of CPU units that are used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.processingjobs.cpuutilization.minimum**(gauge) | The minimum percentage of CPU units that are used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.processingjobs.disk\_utilization**(gauge) | The average percentage of disk space used by the containers on an instance uses.*Shown as percent* | | **aws.sagemaker.processingjobs.disk\_utilization.maximum**(gauge) | The maximum percentage of disk space used by the containers on an instance uses.*Shown as percent* | | **aws.sagemaker.processingjobs.disk\_utilization.minimum**(gauge) | The minimum percentage of disk space used by the containers on an instance uses.*Shown as percent* | | **aws.sagemaker.processingjobs.gpumemory\_utilization**(gauge) | The average percentage of GPU memory used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.processingjobs.gpumemory\_utilization.maximum**(gauge) | The maximum percentage of GPU memory used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.processingjobs.gpumemory\_utilization.minimum**(gauge) | The minimum percentage of GPU memory used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.processingjobs.gpuutilization**(gauge) | The average percentage of GPU units that are used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.processingjobs.gpuutilization.maximum**(gauge) | The maximum percentage of GPU units that are used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.processingjobs.gpuutilization.minimum**(gauge) | The minimum percentage of GPU units that are used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.processingjobs.memory\_utilization**(gauge) | The average percentage of memory that is used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.processingjobs.memory\_utilization.maximum**(gauge) | The maximum percentage of memory that is used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.processingjobs.memory\_utilization.minimum**(gauge) | The minimum percentage of memory that is used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.trainingjobs.cpuutilization**(gauge) | The average percentage of CPU units that are used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.trainingjobs.cpuutilization.maximum**(gauge) | The maximum percentage of CPU units that are used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.trainingjobs.cpuutilization.minimum**(gauge) | The minimum percentage of CPU units that are used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.trainingjobs.disk\_utilization**(gauge) | The average percentage of disk space used by the containers on an instance uses.*Shown as percent* | | **aws.sagemaker.trainingjobs.disk\_utilization.maximum**(gauge) | The maximum percentage of disk space used by the containers on an instance uses.*Shown as percent* | | **aws.sagemaker.trainingjobs.disk\_utilization.minimum**(gauge) | The minimum percentage of disk space used by the containers on an instance uses.*Shown as percent* | | **aws.sagemaker.trainingjobs.gpumemory\_utilization**(gauge) | The average percentage of GPU memory used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.trainingjobs.gpumemory\_utilization.maximum**(gauge) | The maximum percentage of GPU memory used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.trainingjobs.gpumemory\_utilization.minimum**(gauge) | The minimum percentage of GPU memory used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.trainingjobs.gpuutilization**(gauge) | The average percentage of GPU units that are used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.trainingjobs.gpuutilization.maximum**(gauge) | The maximum percentage of GPU units that are used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.trainingjobs.gpuutilization.minimum**(gauge) | The minimum percentage of GPU units that are used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.trainingjobs.memory\_utilization**(gauge) | The average percentage of memory that is used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.trainingjobs.memory\_utilization.maximum**(gauge) | The maximum percentage of memory that is used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.trainingjobs.memory\_utilization.minimum**(gauge) | The minimum percentage of memory that is used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.transformjobs.cpuutilization**(gauge) | The average percentage of CPU units that are used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.transformjobs.cpuutilization.maximum**(gauge) | The maximum percentage of CPU units that are used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.transformjobs.cpuutilization.minimum**(gauge) | The minimum percentage of CPU units that are used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.transformjobs.disk\_utilization**(gauge) | The average percentage of disk space used by the containers on an instance uses.*Shown as percent* | | **aws.sagemaker.transformjobs.disk\_utilization.maximum**(gauge) | The maximum percentage of disk space used by the containers on an instance uses.*Shown as percent* | | **aws.sagemaker.transformjobs.disk\_utilization.minimum**(gauge) | The minimum percentage of disk space used by the containers on an instance uses.*Shown as percent* | | **aws.sagemaker.transformjobs.gpumemory\_utilization**(gauge) | The average percentage of GPU memory used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.transformjobs.gpumemory\_utilization.maximum**(gauge) | The maximum percentage of GPU memory used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.transformjobs.gpumemory\_utilization.minimum**(gauge) | The minimum percentage of GPU memory used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.transformjobs.gpuutilization**(gauge) | The average percentage of GPU units that are used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.transformjobs.gpuutilization.maximum**(gauge) | The maximum percentage of GPU units that are used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.transformjobs.gpuutilization.minimum**(gauge) | The minimum percentage of GPU units that are used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.transformjobs.memory\_utilization**(gauge) | The average percentage of memory that is used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.transformjobs.memory\_utilization.maximum**(gauge) | The maximum percentage of memory that is used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.transformjobs.memory\_utilization.minimum**(gauge) | The minimum percentage of memory that is used by the containers on an instance.*Shown as percent* | | **aws.sagemaker.labelingjobs.dataset\_objects\_auto\_annotated**(count) | The average number of dataset objects auto-annotated in a labeling job. | | **aws.sagemaker.labelingjobs.dataset\_objects\_human\_annotated**(count) | The average number of dataset objects annotated by a human in a labeling job. | | **aws.sagemaker.labelingjobs.dataset\_objects\_labeling\_failed**(count) | The number of dataset objects that failed labeling in a labeling job. | | **aws.sagemaker.labelingjobs.jobs\_succeeded.samplecount**(count) | The sample count of occurrences a single labeling job succeeded.*Shown as job* | | **aws.sagemaker.labelingjobs.jobs\_succeeded.sum**(count) | The sum of occurrences a single labeling job succeeded.*Shown as job* | | **aws.sagemaker.labelingjobs.total\_dataset\_objects\_labeled**(count) | The average number of dataset objects labeled successfully in a labeling job. | | **aws.sagemaker.workteam.active\_workers**(count) | The average number of single active workers on a private work team that submitted, released, or declined a task. | | **aws.sagemaker.workteam.active\_workers.samplecount**(count) | The sample count of single active workers on a private work team that submitted, released, or declined a task. | | **aws.sagemaker.workteam.active\_workers.sum**(count) | The sum of single active workers on a private work team that submitted, released, or declined a task. | | **aws.sagemaker.workteam.tasks\_accepted.samplecount**(count) | The sample count of occurrences a single task was accepted by a worker. | | **aws.sagemaker.workteam.tasks\_accepted.sum**(count) | The sum of occurrences a single task was accepted by a worker. | | **aws.sagemaker.workteam.tasks\_declined.samplecount**(count) | The sample count of occurrences a single task was declined by a worker. | | **aws.sagemaker.workteam.tasks\_declined.sum**(count) | The sum of occurrences a single task was declined by a worker. | | **aws.sagemaker.workteam.tasks\_submitted.samplecount**(count) | The average number of occurrences a single task was submitted/completed by a private worker. | | **aws.sagemaker.workteam.tasks\_submitted.sum**(count) | The average number of occurrences a single task was submitted/completed by a private worker. | | **aws.sagemaker.modelbuildingpipeline.execution\_duration**(gauge) | The average duration in milliseconds that the pipeline execution ran.*Shown as millisecond* | | **aws.sagemaker.modelbuildingpipeline.execution\_duration.maximum**(gauge) | The maximum duration in milliseconds that the pipeline execution ran.*Shown as millisecond* | | **aws.sagemaker.modelbuildingpipeline.execution\_duration.minimum**(gauge) | The minimum duration in milliseconds that the pipeline execution ran.*Shown as millisecond* | | **aws.sagemaker.modelbuildingpipeline.execution\_duration.samplecount**(count) | The sample count duration in milliseconds that the pipeline execution ran.*Shown as millisecond* | | **aws.sagemaker.modelbuildingpipeline.execution\_duration.sum**(gauge) | The sum duration in milliseconds that the pipeline execution ran.*Shown as millisecond* | | **aws.sagemaker.modelbuildingpipeline.execution\_failed**(count) | The average number of steps that failed. | | **aws.sagemaker.modelbuildingpipeline.execution\_failed.sum**(count) | The sum of steps that failed. | | **aws.sagemaker.modelbuildingpipeline.execution\_started**(count) | The average number of pipeline executions that started. | | **aws.sagemaker.modelbuildingpipeline.execution\_started.sum**(count) | The sum of pipeline executions that started. | | **aws.sagemaker.modelbuildingpipeline.execution\_stopped**(count) | The average number of pipeline executions that stopped. | | **aws.sagemaker.modelbuildingpipeline.execution\_stopped.sum**(count) | The sum of pipeline executions that stopped. | | **aws.sagemaker.modelbuildingpipeline.execution\_succeeded**(count) | The average number of pipeline executions that succeeded. | | **aws.sagemaker.modelbuildingpipeline.execution\_succeeded.sum**(count) | The sum of pipeline executions that succeeded. | | **aws.sagemaker.modelbuildingpipeline.step\_duration**(gauge) | The average duration in milliseconds that the step ran.*Shown as millisecond* | | **aws.sagemaker.modelbuildingpipeline.step\_duration.maximum**(gauge) | The maximum duration in milliseconds that the step ran.*Shown as millisecond* | | **aws.sagemaker.modelbuildingpipeline.step\_duration.minimum**(gauge) | The minimum duration in milliseconds that the step ran.*Shown as millisecond* | | **aws.sagemaker.modelbuildingpipeline.step\_duration.samplecount**(count) | The sample count duration in milliseconds that the step ran.*Shown as millisecond* | | **aws.sagemaker.modelbuildingpipeline.step\_duration.sum**(gauge) | The sum duration in milliseconds that the step ran.*Shown as millisecond* | | **aws.sagemaker.modelbuildingpipeline.step\_failed**(count) | The average number of steps that failed. | | **aws.sagemaker.modelbuildingpipeline.step\_failed.sum**(count) | The sum of steps that failed. | | **aws.sagemaker.modelbuildingpipeline.step\_started**(count) | The average number of steps that started. | | **aws.sagemaker.modelbuildingpipeline.step\_started.sum**(count) | The sum of steps that started. | | **aws.sagemaker.modelbuildingpipeline.step\_stopped**(count) | The average number of steps that stopped. | | **aws.sagemaker.modelbuildingpipeline.step\_stopped.sum**(count) | The sum of steps that stopped. | | **aws.sagemaker.modelbuildingpipeline.step\_succeeded**(count) | The average number of steps that succeeded. | | **aws.sagemaker.modelbuildingpipeline.step\_succeeded.sum**(count) | The sum of steps that succeeded. | ### Events{% #events %} The Amazon SageMaker integration does not include any events. ### Service Checks{% #service-checks %} The Amazon SageMaker integration does not include any service checks. ## Out-of-the-box monitoring{% #out-of-the-box-monitoring %} Datadog provides out-of-the-box dashboards for your SageMaker endpoints and jobs. ### SageMaker endpoints{% #sagemaker-endpoints %} Use the [SageMaker endpoints dashboard](https://app.datadoghq.com/dash/integration/31076/amazon-sagemaker-endpoints) to help you immediately start monitoring the health and performance of your SageMaker endpoints with no additional configuration. Determine which endpoints have errors, higher-than-expected latency, or traffic spikes. Review and correct your instance type and scaling policy selections using CPU, GPU, memory, and disk utilization metrics. ### SageMaker jobs{% #sagemaker-jobs %} You can use the [SageMaker jobs dashboard](https://app.datadoghq.com/dash/integration/31077/amazon-sagemaker-jobs) to gain insight into the resource utilization (for example, finding CPU, GPU, and storage bottlenecks) of your training, processing, or transform jobs. Use this information to optimize your compute instances. ## Troubleshooting{% #troubleshooting %} Need help? Contact [Datadog support](https://docs.datadoghq.com/help/). ## Further Reading{% #further-reading %} - [CloudHealth + Datadog: Effectively manage your cloud assets](https://www.datadoghq.com/blog/monitor-cloudhealth-assets-datadog/)