Amazon Personalize Update: [Personalize] Adds support for returning hyperparameter values of the best performing model in a HPO job.

Author: AWS

.changes/next-release/feature-AmazonPersonalize-da9c608.json

@@ -0,0 +1,5 @@
+{
+    "type": "feature",
+    "category": "Amazon Personalize",
+    "description": "[Personalize] Adds support for returning hyperparameter values of the best performing model in a HPO job."
+}

services/personalize/src/main/resources/codegen-resources/service-2.json

@@ -816,6 +816,10 @@
         "failureReason":{
           "shape":"FailureReason",
           "documentation":"<p>If the batch inference job failed, the reason for the failure.</p>"
+        },
+        "solutionVersionArn":{
+          "shape":"Arn",
+          "documentation":"<p>The ARN of the solution version used by the batch inference job.</p>"
         }
       },
       "documentation":"<p>A truncated version of the <a>BatchInferenceJob</a> datatype. The <a>ListBatchInferenceJobs</a> operation returns a list of batch inference job summaries.</p>"
@@ -2130,7 +2134,7 @@
       "members":{
         "type":{
           "shape":"HPOObjectiveType",
-          "documentation":"<p>The data type of the metric.</p>"
+          "documentation":"<p>The type of the metric. Valid values are <code>Maximize</code> and <code>Minimize</code>.</p>"
         },
         "metricName":{
           "shape":"MetricName",
@@ -2871,6 +2875,10 @@
           "shape":"TrainingMode",
           "documentation":"<p>The scope of training used to create the solution version. The <code>FULL</code> option trains the solution version based on the entirety of the input solution's training data, while the <code>UPDATE</code> option processes only the training data that has changed since the creation of the last solution version. Choose <code>UPDATE</code> when you want to start recommending items added to the dataset without retraining the model.</p> <important> <p>The <code>UPDATE</code> option can only be used after you've created a solution version with the <code>FULL</code> option and the training solution uses the <a>native-recipe-hrnn-coldstart</a>.</p> </important>"
         },
+        "tunedHPOParams":{
+          "shape":"TunedHPOParams",
+          "documentation":"<p>If hyperparameter optimization was performed, contains the hyperparameter values of the best performing model.</p>"
+        },
         "status":{
           "shape":"Status",
           "documentation":"<p>The status of the solution version.</p> <p>A solution version can be in one of the following states:</p> <ul> <li> <p>CREATE PENDING</p> </li> <li> <p>CREATE IN_PROGRESS</p> </li> <li> <p>ACTIVE</p> </li> <li> <p>CREATE FAILED</p> </li> </ul>"
@@ -2954,6 +2962,16 @@
       "min":1
     },
     "Tunable":{"type":"boolean"},
+    "TunedHPOParams":{
+      "type":"structure",
+      "members":{
+        "algorithmHyperParameters":{
+          "shape":"HyperParameters",
+          "documentation":"<p>A list of the hyperparameter values of the best performing model.</p>"
+        }
+      },
+      "documentation":"<p>If hyperparameter optimization (HPO) was performed, contains the hyperparameter values of the best performing model.</p>"
+    },
     "UpdateCampaignRequest":{
       "type":"structure",
       "required":["campaignArn"],