minor readme edits (#290)

README.md

@@ -33,12 +33,12 @@ To create or setup a workspace with the assets used in these examples, run the [
 
 ## Getting started
 
-To get started, try the [introductory tutorial](tutorials/an-introduction). You will:
+To get started, see the [introductory tutorial](tutorials/an-introduction) which uses AML to:
 
-- run a `"hello world"` job on cloud compute to demonstrate the basics
-- run a series of PyTorch training jobs on cloud compute to demonstrate mlflow tracking & using cloud data
+- run a `"hello world"` job on cloud compute, demonstrating the basics
+- run a series of PyTorch training jobs on cloud compute, demonstrating mlflow tracking & using cloud data
 
-You should then be able to understand every other example available in the repository, which are listed below.
+These concepts are sufficient to understand all examples in this repository, which are listed below.
 
 ## Contents
 

prefix.md

@@ -33,12 +33,12 @@ To create or setup a workspace with the assets used in these examples, run the [
 
 ## Getting started
 
-To get started, try the [introductory tutorial](tutorials/an-introduction). You will:
+To get started, see the [introductory tutorial](tutorials/an-introduction) which uses AML to:
 
-- run a `"hello world"` job on cloud compute to demonstrate the basics
-- run a series of PyTorch training jobs on cloud compute to demonstrate mlflow tracking & using cloud data
+- run a `"hello world"` job on cloud compute, demonstrating the basics
+- run a series of PyTorch training jobs on cloud compute, demonstrating mlflow tracking & using cloud data
 
-You should then be able to understand every other example available in the repository, which are listed below.
+These concepts are sufficient to understand all examples in this repository, which are listed below.
 
 ## Contents
 

tutorials/an-introduction/README.md

@@ -2,12 +2,12 @@
 
 description: learn the basics of Azure Machine Learning
 
-This tutorial walks through the day 1 steps for getting started with Azure Machine Learning by running through a "hello world" and then basic PyTorch training on remote compute.
+This tutorial walks through the day 1 steps for getting started with Azure Machine Learning by running through a "hello world" and then basic PyTorch training on remote compute, introducing the steps to move a workflow to the cloud.
 
 The tutorial consists of three notebooks:
 
-1. [1.hello-world.ipynb](1.hello-world.ipynb)
-1. [2.pytorch-model.ipynb](2.pytorch-model.ipynb)
-1. [3.pytorch-model-cloud-data.ipynb](3.pytorch-model-cloud-data.ipynb)
+- [1.hello-world.ipynb](1.hello-world.ipynb)
+- [2.pytorch-model.ipynb](2.pytorch-model.ipynb)
+- [3.pytorch-model-cloud-data.ipynb](3.pytorch-model-cloud-data.ipynb)
 
-After running through these, the basic concepts used throughout this repository are demonstrated. You can then go through other tutorials to learn the specifics of Azure cloud integration with various ML tools, or easily run one of the many workflow examples. If you want to run your script regularly or on triggers, see the [template](https://github.com/Azure/azureml-template) setup guide.
+After running through these, the basic concepts used throughout this repository are demonstrated. You can then go through other tutorials to learn the specifics of Azure cloud integration with various ML tools, or easily run one of the workflow examples. If you want to run your script regularly or on triggers, see the [template](https://github.com/Azure/azureml-template) setup guide.

tutorials/using-dask/2.eds-at-scale.ipynb

@@ -479,4 +479,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}