refine description of Introduction workshop

README.md

@@ -9,7 +9,7 @@ Amazon SageMaker is a fully managed service that enables developers and data sci
 
 # Workshops
 
-- [**Introduction to Amazon SageMaker**](Introduction) - This 100-200 level workshop demonstrates some of the key features of Amazon SageMaker.  It does so via a set of straightforward examples for common use cases including: working with structured (tabular) data, natural language processing (sentiment analysis), and computer vision (image classification).  Content includes how to (1) do exploratory data analysis in Amazon SageMaker notebooks; (2) run local and hosted training jobs with your own custom models or built-in algorithms; and (3) get predictions using hosted model endpoints and batch transform jobs.
+- [**Introduction to Amazon SageMaker**](Introduction) - This 100-200 level workshop demonstrates some of the key features of Amazon SageMaker.  It does so via a set of straightforward examples for common use cases including: working with structured (tabular) data, natural language processing (sentiment analysis), and computer vision (image classification).  Content includes how to (1) do exploratory data analysis in Amazon SageMaker notebook environments such as SageMaker Studio or SageMaker Notebook Instances; (2) run Amazon SageMaker training jobs with your own custom models or built-in algorithms; and (3) get predictions using hosted model endpoints and batch transform jobs.
 
 - [**TensorFlow in Amazon SageMaker**](TensorFlow) - In this 400 level workshop for experienced TensorFlow users, various aspects of TensorFlow usage in Amazon SageMaker will be demonstrated.  In particular, TensorFlow will be applied to a natural language processing use case, a structured data use case, and a computer vision use case.  Relevant Amazon SageMaker features that will be demonstrated include:  prototyping with Local Mode training and endpoints, hosted training jobs for full-scale training, distributed training with parameter servers and Horovod, Automatic Model Tuning, batch inference, and hosted endpoints for real time inference.