Autopilot APIs will now support holiday featurization for Timeseries models. The models will now hold holiday metadata and should be able to accommodate holiday effect during inference.

Author: aws-sdk-dotnet-automation

generator/ServiceModels/sagemaker/sagemaker-2017-07-24.api.json

@@ -5652,6 +5652,12 @@
         "CompleteOnConvergence":{"shape":"CompleteOnConvergence"}
       }
     },
+    "CountryCode":{
+      "type":"string",
+      "max":2,
+      "min":2,
+      "pattern":"[A-Z]{2}"
+    },
     "CreateActionRequest":{
       "type":"structure",
       "required":[
@@ -11126,6 +11132,18 @@
       "max":10,
       "min":1
     },
+    "HolidayConfig":{
+      "type":"list",
+      "member":{"shape":"HolidayConfigAttributes"},
+      "max":1,
+      "min":1
+    },
+    "HolidayConfigAttributes":{
+      "type":"structure",
+      "members":{
+        "CountryCode":{"shape":"CountryCode"}
+      }
+    },
     "HookParameters":{
       "type":"map",
       "key":{"shape":"ConfigKey"},
@@ -19748,7 +19766,8 @@
         "ForecastHorizon":{"shape":"ForecastHorizon"},
         "ForecastQuantiles":{"shape":"ForecastQuantiles"},
         "Transformations":{"shape":"TimeSeriesTransformations"},
-        "TimeSeriesConfig":{"shape":"TimeSeriesConfig"}
+        "TimeSeriesConfig":{"shape":"TimeSeriesConfig"},
+        "HolidayConfig":{"shape":"HolidayConfig"}
       }
     },
     "TimeSeriesForecastingSettings":{

generator/ServiceModels/sagemaker/sagemaker-2017-07-24.docs.json

@@ -11,8 +11,8 @@
     "CreateApp": "<p>Creates a running app for the specified UserProfile. This operation is automatically invoked by Amazon SageMaker Studio upon access to the associated Domain, and when new kernel configurations are selected by the user. A user may have multiple Apps active simultaneously.</p>",
     "CreateAppImageConfig": "<p>Creates a configuration for running a SageMaker image as a KernelGateway app. The configuration specifies the Amazon Elastic File System (EFS) storage volume on the image, and a list of the kernels in the image.</p>",
     "CreateArtifact": "<p>Creates an <i>artifact</i>. An artifact is a lineage tracking entity that represents a URI addressable object or data. Some examples are the S3 URI of a dataset and the ECR registry path of an image. For more information, see <a href=\"https://docs.aws.amazon.com/sagemaker/latest/dg/lineage-tracking.html\">Amazon SageMaker ML Lineage Tracking</a>.</p>",
-    "CreateAutoMLJob": "<p>Creates an Autopilot job also referred to as Autopilot experiment or AutoML job.</p> <note> <p>We recommend using the new versions <a href=\"https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJobV2.html\">CreateAutoMLJobV2</a> and <a href=\"https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJobV2.html\">DescribeAutoMLJobV2</a>, which offer backward compatibility.</p> <p> <code>CreateAutoMLJobV2</code> can manage tabular problem types identical to those of its previous version <code>CreateAutoMLJob</code>, as well as non-tabular problem types such as image or text classification.</p> <p>Find guidelines about how to migrate a <code>CreateAutoMLJob</code> to <code>CreateAutoMLJobV2</code> in <a href=\"https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development-create-experiment-api.html#autopilot-create-experiment-api-migrate-v1-v2\">Migrate a CreateAutoMLJob to CreateAutoMLJobV2</a>.</p> </note> <p>You can find the best-performing model after you run an AutoML job by calling <a href=\"https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJobV2.html\">DescribeAutoMLJobV2</a> (recommended) or <a href=\"https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJob.html\">DescribeAutoMLJob</a>.</p>",
-    "CreateAutoMLJobV2": "<p>Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2.</p> <note> <p> <a href=\"https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJobV2.html\">CreateAutoMLJobV2</a> and <a href=\"https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJobV2.html\">DescribeAutoMLJobV2</a> are new versions of <a href=\"https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJob.html\">CreateAutoMLJob</a> and <a href=\"https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJob.html\">DescribeAutoMLJob</a> which offer backward compatibility.</p> <p> <code>CreateAutoMLJobV2</code> can manage tabular problem types identical to those of its previous version <code>CreateAutoMLJob</code>, as well as non-tabular problem types such as image or text classification.</p> <p>Find guidelines about how to migrate a <code>CreateAutoMLJob</code> to <code>CreateAutoMLJobV2</code> in <a href=\"https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development-create-experiment-api.html#autopilot-create-experiment-api-migrate-v1-v2\">Migrate a CreateAutoMLJob to CreateAutoMLJobV2</a>.</p> </note> <p>For the list of available problem types supported by <code>CreateAutoMLJobV2</code>, see <a href=\"https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLProblemTypeConfig.html\">AutoMLProblemTypeConfig</a>.</p> <p>You can find the best-performing model after you run an AutoML job V2 by calling <a href=\"https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJobV2.html\">DescribeAutoMLJobV2</a>.</p>",
+    "CreateAutoMLJob": "<p>Creates an Autopilot job also referred to as Autopilot experiment or AutoML job.</p> <note> <p>We recommend using the new versions <a href=\"https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJobV2.html\">CreateAutoMLJobV2</a> and <a href=\"https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJobV2.html\">DescribeAutoMLJobV2</a>, which offer backward compatibility.</p> <p> <code>CreateAutoMLJobV2</code> can manage tabular problem types identical to those of its previous version <code>CreateAutoMLJob</code>, as well as time-series forecasting, and non-tabular problem types such as image or text classification.</p> <p>Find guidelines about how to migrate a <code>CreateAutoMLJob</code> to <code>CreateAutoMLJobV2</code> in <a href=\"https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development-create-experiment-api.html#autopilot-create-experiment-api-migrate-v1-v2\">Migrate a CreateAutoMLJob to CreateAutoMLJobV2</a>.</p> </note> <p>You can find the best-performing model after you run an AutoML job by calling <a href=\"https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJobV2.html\">DescribeAutoMLJobV2</a> (recommended) or <a href=\"https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJob.html\">DescribeAutoMLJob</a>.</p>",
+    "CreateAutoMLJobV2": "<p>Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2.</p> <note> <p> <a href=\"https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJobV2.html\">CreateAutoMLJobV2</a> and <a href=\"https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJobV2.html\">DescribeAutoMLJobV2</a> are new versions of <a href=\"https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJob.html\">CreateAutoMLJob</a> and <a href=\"https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJob.html\">DescribeAutoMLJob</a> which offer backward compatibility.</p> <p> <code>CreateAutoMLJobV2</code> can manage tabular problem types identical to those of its previous version <code>CreateAutoMLJob</code>, as well as time-series forecasting, and non-tabular problem types such as image or text classification.</p> <p>Find guidelines about how to migrate a <code>CreateAutoMLJob</code> to <code>CreateAutoMLJobV2</code> in <a href=\"https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development-create-experiment-api.html#autopilot-create-experiment-api-migrate-v1-v2\">Migrate a CreateAutoMLJob to CreateAutoMLJobV2</a>.</p> </note> <p>For the list of available problem types supported by <code>CreateAutoMLJobV2</code>, see <a href=\"https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLProblemTypeConfig.html\">AutoMLProblemTypeConfig</a>.</p> <p>You can find the best-performing model after you run an AutoML job V2 by calling <a href=\"https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJobV2.html\">DescribeAutoMLJobV2</a>.</p>",
     "CreateCodeRepository": "<p>Creates a Git repository as a resource in your SageMaker account. You can associate the repository with notebook instances so that you can use Git source control for the notebooks you create. The Git repository is a resource in your SageMaker account, so it can be associated with more than one notebook instance, and it persists independently from the lifecycle of any notebook instances it is associated with.</p> <p>The repository can be hosted either in <a href=\"https://docs.aws.amazon.com/codecommit/latest/userguide/welcome.html\">Amazon Web Services CodeCommit</a> or in any other Git repository.</p>",
     "CreateCompilationJob": "<p>Starts a model compilation job. After the model has been compiled, Amazon SageMaker saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify. </p> <p>If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts with Amazon Web Services IoT Greengrass. In that case, deploy them as an ML resource.</p> <p>In the request body, you provide the following:</p> <ul> <li> <p>A name for the compilation job</p> </li> <li> <p> Information about the input model artifacts </p> </li> <li> <p>The output location for the compiled model and the device (target) that the model runs on </p> </li> <li> <p>The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker assumes to perform the model compilation job. </p> </li> </ul> <p>You can also provide a <code>Tag</code> to track the model compilation job's resource use and costs. The response body contains the <code>CompilationJobArn</code> for the compiled job.</p> <p>To stop a model compilation job, use <a href=\"https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_StopCompilationJob.html\">StopCompilationJob</a>. To get information about a particular model compilation job, use <a href=\"https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeCompilationJob.html\">DescribeCompilationJob</a>. To get information about multiple model compilation jobs, use <a href=\"https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ListCompilationJobs.html\">ListCompilationJobs</a>.</p>",
     "CreateContext": "<p>Creates a <i>context</i>. A context is a lineage tracking entity that represents a logical grouping of other tracking or experiment entities. Some examples are an endpoint and a model package. For more information, see <a href=\"https://docs.aws.amazon.com/sagemaker/latest/dg/lineage-tracking.html\">Amazon SageMaker ML Lineage Tracking</a>.</p>",
@@ -2271,6 +2271,12 @@
         "TuningJobCompletionCriteria$ConvergenceDetected": "<p>A flag to top your hyperparameter tuning job if automatic model tuning (AMT) has detected that your model has converged as evaluated against your objective function.</p>"
       }
     },
+    "CountryCode": {
+      "base": null,
+      "refs": {
+        "HolidayConfigAttributes$CountryCode": "<p>The country code for the holiday calendar.</p> <p>For the list of public holiday calendars supported by AutoML job V2, see <a href=\"https://docs.aws.amazon.com/forecast/latest/dg/holidays.html#holidays-country-codes\">Country Codes</a>. Use the country code corresponding to the country of your choice.</p>"
+      }
+    },
     "CreateActionRequest": {
       "base": null,
       "refs": {
@@ -5819,6 +5825,18 @@
         "OidcMemberDefinition$Groups": "<p>A list of comma seperated strings that identifies user groups in your OIDC IdP. Each user group is made up of a group of private workers.</p>"
       }
     },
+    "HolidayConfig": {
+      "base": null,
+      "refs": {
+        "TimeSeriesForecastingJobConfig$HolidayConfig": "<p>The collection of holiday featurization attributes used to incorporate national holiday information into your forecasting model.</p>"
+      }
+    },
+    "HolidayConfigAttributes": {
+      "base": "<p>Stores the holiday featurization attributes applicable to each item of time-series datasets during the training of a forecasting model. This allows the model to identify patterns associated with specific holidays.</p>",
+      "refs": {
+        "HolidayConfig$member": null
+      }
+    },
     "HookParameters": {
       "base": null,
       "refs": {
@@ -13604,9 +13622,9 @@
       }
     },
     "TimeSeriesForecastingJobConfig": {
-      "base": "<p>The collection of settings used by an AutoML job V2 for the time-series forecasting problem type.</p> <note> <p>The <code>TimeSeriesForecastingJobConfig</code> problem type is only available in private beta. Contact Amazon Web Services Support or your account manager to learn more about access privileges.</p> </note>",
+      "base": "<p>The collection of settings used by an AutoML job V2 for the time-series forecasting problem type.</p>",
       "refs": {
-        "AutoMLProblemTypeConfig$TimeSeriesForecastingJobConfig": "<p>Settings used to configure an AutoML job V2 for a time-series forecasting problem type.</p> <note> <p>The <code>TimeSeriesForecastingJobConfig</code> problem type is only available in private beta. Contact Amazon Web Services Support or your account manager to learn more about access privileges.</p> </note>"
+        "AutoMLProblemTypeConfig$TimeSeriesForecastingJobConfig": "<p>Settings used to configure an AutoML job V2 for a time-series forecasting problem type.</p>"
       }
     },
     "TimeSeriesForecastingSettings": {