--- sourceDocument: Australia IT Operations Management sourceDocumentLink: https://www.servicenow.com/docs/r/australia/it-operations-management Release : - australia ft:locale : - en-US ft:publication_title : - Australia IT Operations Management ft:clusterId : - itom bundleId : - itom workflow : - Technology --- # Amazon SageMaker Training Job # Amazon SageMaker Training Job pattern-based discovery {#ariaid-title1} Release version: Australia Updated March 12, 2026 ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 2 minutes to read Summarize ![AI sparkle icon](https://servicenow.com/docs/portal-asset/ai-sparkle-icon) Summarized using AI This content was generated using new OpenAI-powered functionality. Results are provided on an as is basis and are not guaranteed to be accurate or complete. ## Summary of Amazon SageMaker Training Job pattern-based discovery The Amazon SageMaker Training Job pattern-based discovery enables ServiceNow customers to automatically discover and map Amazon SageMaker training jobs within their AWS cloud environments. This functionality is part of the Discovery and Service Mapping Patterns application, which identifies AWS services and populates data into both CMDB and non-CMDB tables for comprehensive cloud service visibility. Show full answer Show less ## Key Features * **Discovery Prerequisites:** Customers must verify AWS discovery prerequisites and ensure relevant resources are not duplicated by removing them from the Resource Inclusion List table. * **Pattern Activation:** The SageMaker Training Job discovery pattern is disabled by default and must be enabled. Activating or deactivating patterns will not be treated as customizations and will continue to receive updates automatically. * **GovCloud Support:** For AWS GovCloud accounts, discovery requires configuring the data center URL correctly in AWS service accounts. * **Data Population:** The discovery process populates detailed information into specific CMDB and non-CMDB tables, ensuring accurate tracking and management of SageMaker training jobs. * **Tag Collection:** AWS tags associated with SageMaker training jobs are collected and stored in the Key Value table for enhanced identification and classification. ## Data and Configuration Details The pattern populates the following key tables: * **Non-CMDB Table:** *cmdbawssagemakertrainingjob* stores unique identifiers (ARN), job names, statuses, and related ARNs. * **CMDB Table:** *cmdbcicloudappserver* stores core configuration item data including install and operational status, resource type (set as AWS::SageMaker::Training Job), and unique ARNs. * **CI Relationships:** The pattern establishes relationships such as "Hosted on" between SageMaker Training Jobs, Cloud App Servers, and AWS Datacenters to provide accurate dependency mappings. * **Tag Information:** Tags are stored in *cmdbkeyvalue* table with key-value pairs for better resource organization and filtering. ## What This Enables for ServiceNow Customers By leveraging this pattern, customers gain automated, up-to-date discovery and mapping of Amazon SageMaker training jobs within their AWS environments. This visibility supports improved configuration management, operational monitoring, and governance of machine learning workloads. Additionally, the integration with CMDB relationships and tag data enhances service context, enabling better impact analysis and cloud resource management. Discovery and Service Mapping Patterns finds AWS services on your cloud environment. Discovering some of these resources might require updating to the latest version of the Discovery and Service Mapping Patterns application from the ServiceNow Store. ## Pattern-based discovery and mapping requirements {#aws-sagemaker-training-job__selection_ipy_53n_jfc} Verify the AWS discovery prerequisites : For more information, see the prerequisites section in [AWS discovery using patterns](https://www.servicenow.com/docs/NFaTlkGaSxMWb2bQPsCnCA "Discovery and Service Mapping Patterns uses patterns to discover components of the Amazon AWS Cloud deployment during horizontal discovery. Discovering some of these resources might require updating to the latest version of the Discovery and Service Mapping Patterns application from the ServiceNow Store."). Remove resources from the Resource Inclusion List table : Verify that the relevant resource isn't listed in the Resource Inclusion List \[sa_cloud_inventory_resource_whitelist\] table to avoid duplicate discovery. For more information on removing resources from the Resource Inclusion List, see [AWS Resource Inventory discovery with Patterns](https://www.servicenow.com/docs/0pDNq0441mpy~sExfQvvqA "The ServiceNow Discovery and Service Mapping applications can find and map the AWS resources available by AWS Config Service. Discovering some of these resources might require updating to the latest version of the Discovery and Service Mapping Patterns application from the ServiceNow Store."). Enable the relevant pattern : The pattern for this service is disabled by default. Starting with Visibility Content version 6.28.0, activating or deactivating a pattern won't be considered a customization, and it will continue to receive updates. Patterns that were previously activated or deactivated will reset to the latest predefined version after upgrading while retaining the last active field value. For more information on enabling patterns, see [Activate a disabled pattern](https://www.servicenow.com/docs/aypCYVQdO8oFebLMLEI89g "If you want to use a pattern for discovery that's disabled by default, activate it manually.").{#aws-sagemaker-training-job__activating-disabled-pattern-not-customization} Configure the Discovery schedule to support GovCloud : Discovering AWS GovCloud (US) accounts requires using a datacenter URL when setting up an AWS service account. For more information, see [Create AWS service accounts](https://www.servicenow.com/docs/vpueuFRHMWLNi6dzsJv94A "Create AWS service accounts on the ServiceNow AI Platform to access your AWS account during AWS discovery."). Discovery and Service Mapping Patterns application populates data in both CMDB and non-CMDB tables. ## Data stored in non-CMDB tables {#aws-sagemaker-training-job__section_aq4_qkq_hfc} Discovery and Service Mapping Patterns application populates data in the non-CMDB table when running the Amazon AWS - SageMaker Training Job - Extended Inventory (LP) pattern. You can review the non-CMDB AWS tables by navigating to AllConfigurationAWS. You can also search the navigation filter for the specific pattern name. {#aws-sagemaker-training-job__section_aq4_qkq_hfc__entry__2} | Field | Description | |-|-| | Object Id \[object_id\] | The Amazon Resource Name (ARN) uniquely identifying the training job. | | Name \[name\] | The user-defined name for the training job. | | Configuration Item \[configuration_item\] | References the Cloud App Server \[cmdb_ci_cloud_appserver\] table. | | Secondary Status \[secondary_status\] | The additional status information providing more detail about the training job's progress. | | Training Job Status \[training_job_status\] | The current status of the training job. | | Training Plan ARN \[training_plan_arn\] | The ARN of the training plan associated with the job. | [Table 1. AWS SageMaker - Training Job \[cmdb_aws_sagemaker_training_job\]] ## Data stored in CMDB tables Discovery and Service Mapping Patterns application populates data in the CMDB when running the Amazon AWS - SageMaker Training Job - Extended Inventory (LP) pattern. {#aws-sagemaker-training-job__entry__16} | Field | Description | |-|-| | Object ID \[object_id\] | The ARN uniquely identifying the training job. | | Name \[name\] | The user-defined name for the training job. | | Install Status \[install_status\] | Install status of the resource. Default value is Installed. | | Operational status \[operational_status\] | Operational status of the resource. Default value is Operational. | | Type \[type\] | Type of resource. The value is set to AWS::SageMaker::Training Job. | [Table 2. Cloud App Server \[cmdb_ci_cloud_appserver\]] ## CI relationships The Amazon AWS - SageMaker Training Job - Extended Inventory (LP) pattern creates these relationships to support Amazon SageMaker Training Job discovery. {#aws-sagemaker-training-job__entry__29} | CI | Relationship | CI | |-|-|-| | Cloud App Server \[cmdb_ci_cloud_appserver\] | Hosted on::Hosts | AWS Datacenter \[cmdb_ci_aws_datacenter\] | | AWS SageMaker - Training Job \[cmdb_aws_sagemaker_training_job\] | References | Cloud App Server \[cmdb_ci_cloud_appserver\] | [ ] ## AWS tag discovery {#aws-sagemaker-training-job__section_ezc_jwt_jfc} The pattern collects tags and populates them in the Key Value \[cmdb_key_value\] table. {#aws-sagemaker-training-job__table_axq_spc_y2c__entry__2} | Field | Description | |-|-| | Key \[key\] | Tag name. | | Value \[value\] | Tag value. | [Table 3. Key Value \[cmdb_key_value\]] {#aws-sagemaker-training-job__table_axq_spc_y2c} *[\>]: and then