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diff --git a/README.md b/README.md
@@ -8,51 +8,36 @@ The [getting_started/](getting_started/) folder contains a CloudFormation templa
 
 The notebooks provided can also serve as a template to building your own models with your own data. This repository is cloned into the environment so you can explore the more advanced notebooks with this approach as well.
 
-## Amazon Personalize Use Cases examples
+## Amazon Personalize Next Steps
 
-The [core_use_cases/](core_use_cases/) folder contains detailed examples of the following typical use cases.
+The [next_steps/](next_steps/) folder contains detailed examples of the following typical next steps in your Amazon Personalize journey. This folder contains the following advanced content:
 
-* Collaborative filtering based on user-item interaction tables. The intuition behind is that similar users like similar items.
-  - [Offline evaluation with 'hrnn' user-based recommendation.](core_use_cases/personalize_temporal_holdout.ipynb)
-  - [Example of 'sims' item-based recommendation.](core_use_cases/personalize_sims_smell_tests.ipynb)
-  - [How recommendation changes after 'put_events'.](core_use_cases/personalize_putEvents_demo.ipynb)
-* Hybrid recommendation also considering user, item, and event meta-data. The result is to extrapolate to out-of-sample users and items, based on their meta-data features.
-  - [How to use user, item, and event 'meta-data'.](core_use_cases/personalize_metadata_example.ipynb)
-  - [Exploring 'cold-start' or 'future' items.](core_use_cases/personalize_coldstart_demo.ipynb)
 
-## Scalable Operations examples for your Amazon Personalize deployments
+* Core Use Cases.
+  - [User Personalization](/next_steps/core_use_cases/user_personalization)
+  - [Personalize Ranking](/next_steps/core_use_cases/personalize_ranking)
+  - [Batch Recommendations](/next_steps/core_use_cases/batch_recommendations)
+  - [Related Items](/next_steps/core_use_cases/related_items)
 
-The [operations/](operations/) folder contains examples on the following topics:
+* Scalable Operations examples for your Amazon Personalize deployments
+    - [MLOps](/next_steps/operations/ml_ops)
+        - This is a project to showcase how to quickly deploy a Personalize Campaign in a fully automated fashion using AWS Step Functions. To get started navigate to the [ml_ops](operations/ml_ops/) folder and follow the README instructions.
+    - [Lambda Examples](/next_steps/operations/lambda_examples)
+        - This folder starts with a basic example of integrating `put_events` into your Personalize Campaigns by using Lambda functions processing new data from S3. To get started navigate to the [lambda_examples](operations/lambda_examples/) folder and follow the README instructions.
 
-* MLOps
-    - This is a project to showcase how to quickly deploy a Personalize Campaign in a fully automated fashion using AWS Step Functions. To get started navigate to the [ml_ops](operations/ml_ops/) folder and follow the README instructions.
-* Lambda Examples
-    - This folder starts with a basic example of integrating `put_events` into your Personalize Campaigns by using Lambda functions processing new data from S3. To get started navigate to the [lambda_examples](operations/lambda_examples/) folder and follow the README instructions.
 
-## Reference Architectures
+* Workshops
+    - [Workshops/](/next_steps/workshops/) folder contains a list of our most current workshops:
+        - POC in a Box
+        - Re:invent 2019
+        - Immersion Days
 
-The [reference_architectures/](reference_architectures/) folder contains reference architectures for the following industries:
-
-* Retail
-* Media and Entertainment
-* Travel and Hospitality 
-
-## Workshops
-
-The [workshops/](workshops/) folder contains a list of our most current workshops:
-
-* POC in a Box
-* Re:invent 2019
-
-## Data Science Tools
-
-The [data_science/](data_science/) folder contains an example on how to approach visualization of the key properties of your input datasets.
-
-The key components we look out for include:
-- Missing data, duplicated events, and repeated item consumptions
-- Power-law distribution of categorical fields
-- Temporal drift analysis for cold-start applicability
-- Analysis on user-session distribution
+* Data Science Tools
+    - The [data_science/](/next_steps/data_science/) folder contains an example on how to approach visualization of the key properties of your input datasets.
+        - Missing data, duplicated events, and repeated item consumptions
+        - Power-law distribution of categorical fields
+        - Temporal drift analysis for cold-start applicability
+        - Analysis on user-session distribution
 
 ## License Summary
 

diff --git a/next_steps/README.md b/next_steps/README.md
@@ -0,0 +1,53 @@
+# Amazon Personalize Next Steps
+
+Notebooks and examples on how to onboard and use various features of Amazon Personalize
+
+## Amazon Personalize Use Cases examples
+
+The [core_use_cases/](core_use_cases/) folder contains detailed examples of the following typical use cases.
+
+* Collaborative filtering based on user-item interaction tables. The intuition behind is that similar users like similar items.
+  - [Offline evaluation with 'hrnn' user-based recommendation.](core_use_cases/personalize_temporal_holdout.ipynb)
+  - [Example of 'sims' item-based recommendation.](core_use_cases/personalize_sims_smell_tests.ipynb)
+  - [How recommendation changes after 'put_events'.](core_use_cases/personalize_putEvents_demo.ipynb)
+* Hybrid recommendation also considering user, item, and event meta-data. The result is to extrapolate to out-of-sample users and items, based on their meta-data features.
+  - [How to use user, item, and event 'meta-data'.](core_use_cases/personalize_metadata_example.ipynb)
+  - [Exploring 'cold-start' or 'future' items.](core_use_cases/personalize_coldstart_demo.ipynb)
+
+## Scalable Operations examples for your Amazon Personalize deployments
+
+The [operations/](operations/) folder contains examples on the following topics:
+
+* MLOps
+    - This is a project to showcase how to quickly deploy a Personalize Campaign in a fully automated fashion using AWS Step Functions. To get started navigate to the [ml_ops](operations/ml_ops/) folder and follow the README instructions.
+* Lambda Examples
+    - This folder starts with a basic example of integrating `put_events` into your Personalize Campaigns by using Lambda functions processing new data from S3. To get started navigate to the [lambda_examples](operations/lambda_examples/) folder and follow the README instructions.
+
+## Reference Architectures
+
+The [reference_architectures/](reference_architectures/) folder contains reference architectures for the following industries:
+
+* Retail
+* Media and Entertainment
+* Travel and Hospitality 
+
+## Workshops
+
+The [workshops/](workshops/) folder contains a list of our most current workshops:
+
+* POC in a Box
+* Re:invent 2019
+
+## Data Science Tools
+
+The [data_science/](data_science/) folder contains an example on how to approach visualization of the key properties of your input datasets.
+
+The key components we look out for include:
+- Missing data, duplicated events, and repeated item consumptions
+- Power-law distribution of categorical fields
+- Temporal drift analysis for cold-start applicability
+- Analysis on user-session distribution
+
+## License Summary
+
+This sample code is made available under a modified MIT license. See the LICENSE file.
diff --git a/core_use_cases/README.md → next_steps/core_use_cases/README.md b/core_use_cases/README.md → next_steps/core_use_cases/README.md
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diff --git a/...OC_in_a_box/completed/PersonalizePOC.yaml → ...OC_in_a_box/completed/PersonalizePOC.yaml b/...OC_in_a_box/completed/PersonalizePOC.yaml → ...OC_in_a_box/completed/PersonalizePOC.yaml
diff --git a/..._in_a_box/completed/PersonalizePOCEE.yaml → ..._in_a_box/completed/PersonalizePOCEE.yaml b/..._in_a_box/completed/PersonalizePOCEE.yaml → ..._in_a_box/completed/PersonalizePOCEE.yaml
diff --git a/workshops/POC_in_a_box/completed/README.md → ...orkshops/POC_in_a_box/completed/README.md b/workshops/POC_in_a_box/completed/README.md → ...orkshops/POC_in_a_box/completed/README.md
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diff --git a/workshops/README.md → next_steps/workshops/README.md b/workshops/README.md → next_steps/workshops/README.md
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diff --git a/...Reinvent_2019/RI_PersonalizeWorkshop.yaml → ...Reinvent_2019/RI_PersonalizeWorkshop.yaml b/...Reinvent_2019/RI_PersonalizeWorkshop.yaml → ...Reinvent_2019/RI_PersonalizeWorkshop.yaml
diff --git a/...Reinvent_2019/ReInvent2019_Workshop.ipynb → ...Reinvent_2019/ReInvent2019_Workshop.ipynb b/...Reinvent_2019/ReInvent2019_Workshop.ipynb → ...Reinvent_2019/ReInvent2019_Workshop.ipynb
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diff --git a/...2019/static/imgs/personalize_overview.png → ...2019/static/imgs/personalize_overview.png b/...2019/static/imgs/personalize_overview.png → ...2019/static/imgs/personalize_overview.png
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diff --git a/reference_architectures/README.md b/reference_architectures/README.md