Add link to iterative scheduling tutorial

Conceptual_Guide/Part_6-building_complex_pipelines/README.md

@@ -28,8 +28,8 @@
 
 # Building Complex Pipelines: Stable Diffusion
 
-| Navigate to | [Part 5: Building Model Ensembles](../Part_5-Model_Ensembles/)  | [Documentation: BLS](https://github.com/triton-inference-server/python_backend#business-logic-scripting) |
-| ------------ | --------------- | --------------- |
+| Navigate to | [Part 5: Building Model Ensembles](../Part_5-Model_Ensembles/) | [Part 7: Iterative Scheduling Tutorial](./Part_7-iterative_scheduling) | [Documentation: BLS](https://github.com/triton-inference-server/python_backend#business-logic-scripting) |
+| ------------ | --------------- | --------------- |  --------------- |
 
 **Watch [this explainer video](https://youtu.be/JgP2WgNIq_w) with discusses the pipeline, before proceeding with the example**. This example focuses on showcasing two of Triton Inference Server's features:
 * Using multiple frameworks in the same inference pipeline. Refer [this for more information](https://github.com/triton-inference-server/backend#where-can-i-find-all-the-backends-that-are-available-for-triton) about supported frameworks.

Conceptual_Guide/Part_7-iterative_scheduling/README.md

@@ -28,6 +28,9 @@
 
 # Deploying a GPT-2 Model using Python Backend and Iterative Scheduling
 
+| Navigate to | [Part 6: Building Complex Pipelines: Stable Diffusion](../Part_6-building_complex_pipelines)  | [Documentation: Iterative Scheduling](https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/user_guide/model_configuration.html#iterative-sequences) |
+| ------------ | --------------- | --------------- |
+
 In this tutorial, we will deploy a GPT-2 model using the Python backend and
 demonstrate the
 [iterative scheduling](https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/user_guide/model_configuration.html#iterative-sequences)

Conceptual_Guide/README.md

@@ -39,4 +39,4 @@ Conceptual guides have been designed as an onboarding experience to Triton Infer
 * [Part 4: Accelerating Models](Part_4-inference_acceleration/): Another path towards achieving higher throughput is to accelerate the underlying models. This guide covers SDKs and tools which can be used to accelerate the models.
 * [Part 5: Building Model Ensembles](./Part_5-Model_Ensembles/): Models are rarely used standalone. This guide will cover "how to build a deep learning inference pipeline?"
 * [Part 6: Using the BLS API to build complex pipelines](Part_6-building_complex_pipelines/): Often times there are scenarios where the pipeline requires control flows. Learn how to work with complex pipelines with models deployed on different backends.
-
+* [Part 7: Iterative Scheduling Tutorial](./Part_7-iterative_scheduling): Shows how to use the Triton Iterative Scheduler with a GPT2 model using HuggingFace Transformers.