Merge pull request #2051 from rahul188/ssaboo-sagemaker

[Product Partnerships] Updated Amazon Sagemaker Entity

dashboards/amazon-sagemaker/amazon-sagemaker.json

@@ -18,7 +18,7 @@
             "id": "viz.markdown"
           },
           "rawConfiguration": {
-            "text": "# Amazon SageMaker\nAmazon SageMaker is a fully managed machine learning service. With Amazon SageMaker, data scientists and developers can quickly build and train machine learning models, and then deploy them into a production-ready hosted environment."
+            "text": "# Amazon SageMaker\nA fully managed machine learning service which enables data scientists and developers to quickly build and train machine learning models and deploy them into a production-ready hosted environment."
           }
         },
         {
@@ -171,7 +171,7 @@
             "id": "viz.markdown"
           },
           "rawConfiguration": {
-            "text": "# Amazon SageMaker Inference Endpoints\nAmazon SageMaker Inference Endpoints are a powerful tool to deploy your machine learning models in the cloud and make predictions on new data at scale."
+            "text": "# Amazon SageMaker Inference Endpoints\nEndpoints enables you to deploy machine learning models in the cloud and make predictions on new data at scale."
           }
         },
         {
@@ -340,7 +340,7 @@
             "id": "viz.markdown"
           },
           "rawConfiguration": {
-            "text": "# Training Jobs\nAmazon SageMaker Training Job is a process that involves training a machine learning model on a dataset using specified algorithms and hyperparameters. "
+            "text": "# Training Job\nIt is used to train a machine learning model on a dataset using specified algorithms and hyperparameters."
           }
         },
         {
@@ -355,7 +355,7 @@
             "id": "viz.markdown"
           },
           "rawConfiguration": {
-            "text": "# Transform jobs\nAmazon SageMaker Transform job is a process used for applying a trained machine learning model to large datasets to obtain predictions or inferences, typically in a batch mode, without the need for real-time endpoint deployment."
+            "text": "# Transform Job\nIt is used to apply a trained machine learning model to large datasets to obtain predictions or inferences, typically in a batch mode, without the need for real-time endpoint deployment."
           }
         },
         {
@@ -370,7 +370,7 @@
             "id": "viz.markdown"
           },
           "rawConfiguration": {
-            "text": "# Processing Jobs\nAmazon SageMaker Processing Job is a managed compute environment used for data preprocessing, analysis, or other tasks in machine learning workflows, helping automate and streamline data preparation and analysis before model training."
+            "text": "# Processing Job\nIt lets users perform data pre-processing, post-processing, feature engineering, data validation, model evaluation and interpretation on Amazon SageMaker."
           }
         },
         {
@@ -1032,7 +1032,7 @@
             "id": "viz.markdown"
           },
           "rawConfiguration": {
-            "text": "# Amazon SageMaker Feature Store \nAmazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. "
+            "text": "# Amazon SageMaker Feature Store \nThis is a fully managed, purpose-built repository to store, share and manage features for machine learning (ML) models."
           }
         },
         {
@@ -1304,7 +1304,7 @@
             "id": "viz.markdown"
           },
           "rawConfiguration": {
-            "text": "# Amazon SageMaker Data Bias Metrics\nTo identify bias in the data before expending resources to train models on it, SageMaker Clarify provides data bias metrics that you can compute on raw datasets before training. All of the pretraining metrics are model-agnostic because they do not depend on model outputs and so are valid for any model. "
+            "text": "# Amazon SageMaker Data Bias Metrics\nSageMaker Clarify provides data bias metrics that you can compute on raw datasets before training. All of the pretraining metrics are model-agnostic because they do not depend on model outputs and so are valid for any model. "
           }
         },
         {