feat: update AWS API models

Author: aws-sdk-kotlin-ci

codegen/sdk/aws-models/iot-events.json

@@ -222,7 +222,7 @@
             "traits": {
                 "smithy.api#length": {
                     "min": 0,
-                    "max": 128
+                    "max": 1024
                 }
             }
         },
@@ -1829,7 +1829,7 @@
             "traits": {
                 "smithy.api#length": {
                     "min": 0,
-                    "max": 128
+                    "max": 1024
                 }
             }
         },
@@ -2462,7 +2462,7 @@
             "traits": {
                 "smithy.api#length": {
                     "min": 0,
-                    "max": 128
+                    "max": 1024
                 }
             }
         },

codegen/sdk/aws-models/lookoutequipment.json

@@ -1256,6 +1256,12 @@
                     "traits": {
                         "smithy.api#documentation": "<p>Indicates the status of the <code>CreateInferenceScheduler</code> operation. </p>"
                     }
+                },
+                "ModelQuality": {
+                    "target": "com.amazonaws.lookoutequipment#ModelQuality",
+                    "traits": {
+                        "smithy.api#documentation": "<p>Provides a quality assessment for a model that uses labels. \n         If Lookout for Equipment determines that the\n         model quality is poor based on training metrics, the value is\n         <code>POOR_QUALITY_DETECTED</code>. Otherwise, the value is\n         <code>QUALITY_THRESHOLD_MET</code>. </p>\n         <p>If the model is unlabeled, the model quality can't\n         be assessed and the value of <code>ModelQuality</code> is\n         <code>CANNOT_DETERMINE_QUALITY</code>. In this situation, you can get a model quality\n         assessment by adding labels to the input dataset and retraining the model.</p>\n         <p>For information about using labels with your models, see <a href=\"https://docs.aws.amazon.com/lookout-for-equipment/latest/ug/understanding-labeling.html\">Understanding labeling</a>.</p>\n         <p>For information about improving the quality of a model, see <a href=\"https://docs.aws.amazon.com/lookout-for-equipment/latest/ug/best-practices.html\">Best practices with\n         Amazon Lookout for Equipment</a>.</p>"
+                    }
                 }
             },
             "traits": {
@@ -3265,6 +3271,12 @@
                     "traits": {
                         "smithy.api#documentation": "<p>Configuration information for the model's pointwise model diagnostics.</p>"
                     }
+                },
+                "ModelQuality": {
+                    "target": "com.amazonaws.lookoutequipment#ModelQuality",
+                    "traits": {
+                        "smithy.api#documentation": "<p>Provides a quality assessment for a model that uses labels. If Lookout for Equipment determines that the\n         model quality is poor based on training metrics, the value is\n         <code>POOR_QUALITY_DETECTED</code>. Otherwise, the value is\n         <code>QUALITY_THRESHOLD_MET</code>.</p>\n         <p>If the model is unlabeled, the model quality can't\n         be assessed and the value of <code>ModelQuality</code> is\n         <code>CANNOT_DETERMINE_QUALITY</code>. In this situation, you can get a model quality\n         assessment by adding labels to the input dataset and retraining the model.</p>\n         <p>For information about using labels with your models, see <a href=\"https://docs.aws.amazon.com/lookout-for-equipment/latest/ug/understanding-labeling.html\">Understanding labeling</a>.</p>\n         <p>For information about improving the quality of a model, see <a href=\"https://docs.aws.amazon.com/lookout-for-equipment/latest/ug/best-practices.html\">Best practices with\n         Amazon Lookout for Equipment</a>.</p>"
+                    }
                 }
             },
             "traits": {
@@ -3522,6 +3534,12 @@
                     "traits": {
                         "smithy.api#documentation": "<p>The Amazon S3 output prefix for where Lookout for Equipment saves the pointwise model diagnostics for the model version.</p>"
                     }
+                },
+                "ModelQuality": {
+                    "target": "com.amazonaws.lookoutequipment#ModelQuality",
+                    "traits": {
+                        "smithy.api#documentation": "<p>Provides a quality assessment for a model that uses labels. If Lookout for Equipment determines that the\n         model quality is poor based on training metrics, the value is\n         <code>POOR_QUALITY_DETECTED</code>. Otherwise, the value is\n         <code>QUALITY_THRESHOLD_MET</code>.</p>\n         <p>If the model is unlabeled, the model quality can't\n         be assessed and the value of <code>ModelQuality</code> is\n         <code>CANNOT_DETERMINE_QUALITY</code>. In this situation, you can get a model quality\n         assessment by adding labels to the input dataset and retraining the model.</p>\n         <p>For information about using labels with your models, see <a href=\"https://docs.aws.amazon.com/lookout-for-equipment/latest/ug/understanding-labeling.html\">Understanding labeling</a>.</p>\n         <p>For information about improving the quality of a model, see <a href=\"https://docs.aws.amazon.com/lookout-for-equipment/latest/ug/best-practices.html\">Best practices with\n         Amazon Lookout for Equipment</a>.</p>"
+                    }
                 }
             },
             "traits": {
@@ -6173,6 +6191,29 @@
                 }
             }
         },
+        "com.amazonaws.lookoutequipment#ModelQuality": {
+            "type": "enum",
+            "members": {
+                "QUALITY_THRESHOLD_MET": {
+                    "target": "smithy.api#Unit",
+                    "traits": {
+                        "smithy.api#enumValue": "QUALITY_THRESHOLD_MET"
+                    }
+                },
+                "CANNOT_DETERMINE_QUALITY": {
+                    "target": "smithy.api#Unit",
+                    "traits": {
+                        "smithy.api#enumValue": "CANNOT_DETERMINE_QUALITY"
+                    }
+                },
+                "POOR_QUALITY_DETECTED": {
+                    "target": "smithy.api#Unit",
+                    "traits": {
+                        "smithy.api#enumValue": "POOR_QUALITY_DETECTED"
+                    }
+                }
+            }
+        },
         "com.amazonaws.lookoutequipment#ModelStatus": {
             "type": "enum",
             "members": {
@@ -6291,6 +6332,12 @@
                 },
                 "ModelDiagnosticsOutputConfiguration": {
                     "target": "com.amazonaws.lookoutequipment#ModelDiagnosticsOutputConfiguration"
+                },
+                "ModelQuality": {
+                    "target": "com.amazonaws.lookoutequipment#ModelQuality",
+                    "traits": {
+                        "smithy.api#documentation": "<p>Provides a quality assessment for a model that uses labels. If Lookout for Equipment determines that the\n         model quality is poor based on training metrics, the value is\n         <code>POOR_QUALITY_DETECTED</code>. Otherwise, the value is\n         <code>QUALITY_THRESHOLD_MET</code>.</p>\n         <p>If the model is unlabeled, the model quality can't\n         be assessed and the value of <code>ModelQuality</code> is\n         <code>CANNOT_DETERMINE_QUALITY</code>. In this situation, you can get a model quality\n         assessment by adding labels to the input dataset and retraining the model.</p>\n         <p>For information about using labels with your models, see <a href=\"https://docs.aws.amazon.com/lookout-for-equipment/latest/ug/understanding-labeling.html\">Understanding labeling</a>.</p>\n         <p>For information about improving the quality of a model, see <a href=\"https://docs.aws.amazon.com/lookout-for-equipment/latest/ug/best-practices.html\">Best practices with\n         Amazon Lookout for Equipment</a>.</p>"
+                    }
                 }
             },
             "traits": {
@@ -6423,6 +6470,12 @@
                     "traits": {
                         "smithy.api#documentation": "<p>Indicates how this model version was generated.</p>"
                     }
+                },
+                "ModelQuality": {
+                    "target": "com.amazonaws.lookoutequipment#ModelQuality",
+                    "traits": {
+                        "smithy.api#documentation": "<p>Provides a quality assessment for a model that uses labels. If Lookout for Equipment determines that the\n         model quality is poor based on training metrics, the value is\n         <code>POOR_QUALITY_DETECTED</code>. Otherwise, the value is\n         <code>QUALITY_THRESHOLD_MET</code>. </p>\n         <p>If the model is unlabeled, the model quality can't\n         be assessed and the value of <code>ModelQuality</code> is\n         <code>CANNOT_DETERMINE_QUALITY</code>. In this situation, you can get a model quality\n         assessment by adding labels to the input dataset and retraining the model.</p>\n         <p>For information about improving the quality of a model, see <a href=\"https://docs.aws.amazon.com/lookout-for-equipment/latest/ug/best-practices.html\">Best practices with\n         Amazon Lookout for Equipment</a>.</p>"
+                    }
                 }
             },
             "traits": {