made revisions and updated README

Author: paul-cb

sagemaker-featurestore/README.md

@@ -1,10 +1,17 @@
 # Amazon SageMaker Feature Store
 
-
 ## Introduction to Feature Store
 In `feature_store_introduction.ipynb` we demonstrate how to get started with Feature Store, create feature groups, and ingest data into them.
 
-This notebook requires these data sets, `./data/feature_store_introduction_customer.csv`, `./data/feature_store_introduction_orders.csv`, and uses these images, `./images/feature-store-policy.png`, `./images/feature_store_data_ingest.svg`.
+This notebook requires these data sets in `./data/`:
+
+* `feature_store_introduction_customer.csv`
+* `feature_store_introduction_orders.csv`
+
+This notebook requires these images in `./images/:
+
+* `feature-store-policy.png`
+* `feature_store_data_ingest.svg`.
 
 
 ## Fraud Detection with Feature Store

sagemaker-featurestore/feature_store_introduction.ipynb

@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This notebook demonstrates how to get started with Feature Store, create feature groups, and ingest data into them. These feature groups are stored in your Feature Store. \n",
+    "This notebook demonstrates how to get started with Feature Store, create feature groups, and ingest data into them. These feature groups are stored in your Feature Store.\n",
     "\n",
     "Feature groups are resources that contain metadata for all data stored in your Feature Store. A feature group is a logical grouping of features, defined in the feature store to describe records. A feature group’s definition is composed of a list of feature definitions, a record identifier name, and configurations for its online and offline store. \n",
     "\n",
@@ -121,7 +121,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Below is an illustration on the steps the data goes through before it is ingested into a Feature Store."
+    "Below is an illustration on the steps the data goes through before it is ingested into a Feature Store. In this notebook, we illustrate the use-case where you have data from multiple sources and want to store them independently in a feature store. Our example considers data from a data warehouse (customer data), and data from a real-time streaming service (order data). "
    ]
   },
   {