Table: aws_sagemaker_model - Query AWS SageMaker Models using SQL

An AWS SageMaker Model in Amazon SageMaker represents the Amazon S3 location where model artifacts are stored, and the Docker registry path where the image that contains the inference code is stored. These models are immutable and can be used for multiple purposes such as predictions, transformations, and associations. SageMaker model provides the entry point for services to access the model artifacts and image.

Table Usage Guide

The aws_sagemaker_model table in Steampipe provides you with information about models within AWS SageMaker. This table allows you, as a DevOps engineer, to query model-specific details, including the model name, ARN, creation time, execution role, and more. You can utilize this table to gather insights on models, such as their associated containers, data input configurations, and VPC configurations. The schema outlines the various attributes of the SageMaker model for you, including the model ARN, creation time, model name, and associated tags.

Examples

Basic info

Explore the settings of the AWS SageMaker model to understand its network isolation status and the time it was created. This can help in auditing and managing your machine learning models effectively.

select
  name,
  arn,
  creation_time,
  enable_network_isolation
from
  aws_sagemaker_model;

select
  name,
  arn,
  creation_time,
  enable_network_isolation
from
  aws_sagemaker_model;

List network isolated models

Determine the areas in which network isolation is enabled within SageMaker models. This is useful for ensuring security and data privacy by preventing any unnecessary network access to these models.

select
  name,
  arn,
  creation_time,
  enable_network_isolation
from
  aws_sagemaker_model
where
  enable_network_isolation;

select
  name,
  arn,
  creation_time,
  enable_network_isolation
from
  aws_sagemaker_model
where
  enable_network_isolation = 1;

Control examples

Schema for aws_sagemaker_model

Name	Type	Operators	Description
_ctx	jsonb		Steampipe context in JSON form.
account_id	text	=, !=, ~~, ~~, !~~, !~~	The AWS Account ID in which the resource is located.
akas	jsonb		Array of globally unique identifier strings (also known as) for the resource.
arn	text		The Amazon Resource Name (ARN) of the model.
containers	jsonb		The containers in the inference pipeline.
creation_time	timestamp with time zone	>, >=, <, <=	A timestamp that indicates when the model was created.
deployment_recommendation	jsonb		A set of recommended deployment configurations for the model.
enable_network_isolation	boolean		If True, no inbound or outbound network calls can be made to or from the model container.
execution_role_arn	text		The Amazon Resource Name (ARN) of the IAM role that you specified for the model.
inference_execution_config	jsonb		Specifies details of how containers in a multi-container endpoint are called.
name	text	=	The name of the model.
partition	text		The AWS partition in which the resource is located (aws, aws-cn, or aws-us-gov).
primary_container	jsonb		The location of the primary inference code, associated artifacts, and custom environment map that the inference code uses when it is deployed in production.
region	text		The AWS Region in which the resource is located.
sp_connection_name	text	=, !=, ~~, ~~, !~~, !~~	Steampipe connection name.
sp_ctx	jsonb		Steampipe context in JSON form.
tags	jsonb		A map of tags for the resource.
tags_src	jsonb		The list of tags for the model.
title	text		Title of the resource.
vpc_config	jsonb		A VpcConfig object that specifies the VPC that this model has access to.

Export

This table is available as a standalone Exporter CLI. Steampipe exporters are stand-alone binaries that allow you to extract data using Steampipe plugins without a database.

You can download the tarball for your platform from the Releases page, but it is simplest to install them with the steampipe_export_installer.sh script:

/bin/sh -c "$(curl -fsSL https://steampipe.io/install/export.sh)" -- aws

You can pass the configuration to the command with the --config argument:

steampipe_export_aws --config '<your_config>' aws_sagemaker_model