More descriptions for data scientists in module 0

Signed-off-by: Danny Chiao <[email protected]>

module_0/README.md

@@ -528,10 +528,11 @@ There are two ways data scientists can use Feast:
   - This is **not recommended** since data scientists cannot register feature services to indicate they depend on certain features in production. 
 - **[Recommended]** Have a local copy of the feature repository (e.g. `git clone`) and author / iterate / re-use features. 
   - Data scientist can:
-    1. iterate on features locally
-    2. apply features to their own dev project with a local registry & experiment
-    3. build feature services in preparation for production
-    4. submit PRs to include features that should be used in production (including A/B experiments, or model training iterations)
+    1. browse relevant features that are already productionized to re-use
+    2. iterate on new features locally
+    3. apply features to their own dev project with a local registry & experiment
+    4. build feature services in preparation for production
+    5. submit PRs to include features that should be used in production (including A/B experiments, or model training iterations)
 
 Data scientists can also investigate other models and their dependent features / data sources / on demand transformations through the repository or through the Web UI (by running `feast ui`)