Update README.md

Author: ddavydenko

README.md

@@ -1,11 +1,11 @@
-# Sagemaker Debugger
+# Amazon SageMaker Debugger
 
 - [Overview](#overview)
 - [Examples](#sagemaker-example)
 - [How It Works](#how-it-works)
 
 ## Overview
-Sagemaker Debugger is an AWS service to automatically debug your machine learning training process.
+Amazon SageMaker Debugger is an AWS service to automatically debug your machine learning training process.
 It helps you develop better, faster, cheaper models by catching common errors quickly. It supports
 TensorFlow, PyTorch, MXNet, and XGBoost on Python 3.6+.
 
@@ -20,7 +20,7 @@ There are two ways to use it: Automatic mode and configurable mode.
 - Configurable mode: More powerful, lets you specify exactly which tensors and collections to save. Use the Python API within your script.
 
 
-## Example: SageMaker Zero-Code-Change
+## Example: Amazon SageMaker Zero-Code-Change
 This example uses a zero-script-change experience, where you can use your training script as-is.
 See the [example notebooks](https://link.com) for more details.
 ```python
@@ -51,7 +51,7 @@ sagemaker_simple_estimator = sagemaker.tensorflow.TensorFlow(
 sagemaker_simple_estimator.fit()
 ```
 
-That's it! SageMaker will automatically monitor your training job for your and create a CloudWatch
+That's it! Amazon SageMaker will automatically monitor your training job for your and create a CloudWatch
 event if you run into exploding tensor values.
 
 If you want greater configuration and control, we offer that too. Simply
@@ -63,7 +63,7 @@ Requires Python 3.6+, and this example uses tf.keras. Run
 pip install smdebug
 ```
 
-To use Sagemaker Debugger, simply add a callback hook:
+To use Amazon SageMaker Debugger, simply add a callback hook:
 ```python
 import smdebug.tensorflow as smd
 hook = smd.KerasHook.(out_dir=args.out_dir)
@@ -85,13 +85,13 @@ print(f"Loss values were {trial.tensor('CrossEntropyLoss:0')}")
 ```
 
 ## How It Works
-SageMaker Debugger uses a `hook` to store the values of tensors throughout the training process. Another process called a `rule` job
+Amazon SageMaker Debugger uses a `hook` to store the values of tensors throughout the training process. Another process called a `rule` job
 simultaneously monitors and validates these outputs to ensure that training is progressing as expected.
 A rule might check for vanishing gradients, or exploding tensor values, or poor weight initialization.
 If a rule is triggered, it will raise a CloudWatch event and stop the training job, saving you time
 and money.
 
-SageMaker Debugger can be used inside or outside of SageMaker. There are three main use cases:
+Amazon SageMaker Debugger can be used inside or outside of SageMaker. There are three main use cases:
 - SageMaker Zero-Script-Change: Here you specify which rules to use when setting up the estimator and run your existing script, no changes needed. See the first example above.
 - SageMaker Bring-Your-Own-Container: Here you specify the rules to use, and modify your training script.
 - Non-SageMaker: Here you write custom rules (or manually analyze the tensors) and modify your training script. See the second example above.