Merge pull request #444 from chuckb1300/main

resolving broken links & removing "refer to footfall page"

docs/azure_jumpstart_ag/contoso_hypermarket/_index.md

@@ -14,7 +14,7 @@ By integrating advanced computer vision into their retail facilities, Contoso Hy
 
 The AI-powered solutions also enhance maintenance and operational efficiency. With real-time monitoring and analytics, Contoso Hypermarket can detect errors or anomalies with automated checkout activities, manage equipment and detect malfunctions. This innovative approach drives operational excellence, positioning Contoso Hypermarket as a leader in the retail sector.
 
-> **Disclaimer:** This Jumpstart Agora scenario utilizes both Azure OpenAI and the _Phi-3-Mini-4K-Instruct_ model from Microsoft to enhance its capabilities in natural language processing and instruction-based interactions. The _Phi-3-Mini-4K-Instruct_ model is licensed under the MIT License, and users are encouraged to review the full license terms. For details, please refer to Arc Jumpstart [MIT License](../../../LICENSE) included in this repository.
+> **Disclaimer:** This Jumpstart Agora scenario utilizes both Azure OpenAI and the _Phi-3-Mini-4K-Instruct_ model from Microsoft to enhance its capabilities in natural language processing and instruction-based interactions. The _Phi-3-Mini-4K-Instruct_ model is licensed under the MIT License, and users are encouraged to review the full license terms. For details, please refer to Arc Jumpstart [MIT License](https://github.com/Azure/arc_jumpstart_docs/blob/main/LICENSE) included in this repository.
 > Additionally, this project uses Intel OpenVINO models, which are distributed under the Apache License 2.0. Please refer to the [Intel OpenVINO License](https://github.com/openvinotoolkit/openvino/blob/master/LICENSE) for the applicable terms and conditions governing its usage.
 
 ## Architecture and technology stack

docs/azure_jumpstart_ag/contoso_hypermarket/observability/infrastructure/_index.md

@@ -41,7 +41,7 @@ Grafana's dashboards in Contoso's implementation provide a visually appealing an
 
 To view the health of the Kubernetes clusters, access the Grafana dashboards by doing the following:
 
-- Connect to the Client VM _Ag-VM-Client_ using the instruction in the [deployment guide](../deployment/)
+- Connect to the Client VM _Ag-VM-Client_ using the instruction in the [deployment guide](../../deployment)
 
 - Open the Edge browser, expand Grafana in the Favorites Bar and select **Grafana**
 

docs/azure_jumpstart_ag/contoso_hypermarket/predictive_analytics/_index.md

@@ -38,7 +38,7 @@ This scenario requires access to Microsoft 365 Copilot.  For information on enab
 To begin, download the three spreadsheets:
 
 - [*retail_inventory_sample*](https://download.microsoft.com/download/0832a0b6-bf27-4a3f-bf65-b3404233f9cb/retail_inventory_sample_20241025.xlsx) - this simulates inventory and sales data for Contoso Hypermarket.
-- [*footfall_sample*](https://download.microsoft.com/download/3fe05ab3-2aa7-4c59-8260-d90a92888432/footfall_sample_20241025.xlsx) - this simulates footfall data derived from in-store cameras.  Footfall is the traffic count inside the store and is useful for store managers to understand what parts of the store get the most visitors.  Refer to the [Contoso Hypermarket footfall documentation](../footfall/) to understand how footfall is implemented in this scenario.
+- [*footfall_sample*](https://download.microsoft.com/download/3fe05ab3-2aa7-4c59-8260-d90a92888432/footfall_sample_20241025.xlsx) - this simulates footfall data derived from in-store cameras.  Footfall is the traffic count inside the store and is useful for store managers to understand what parts of the store get the most visitors.
 - [*contoso_roasters*](https://download.microsoft.com/download/3fe05ab3-2aa7-4c59-8260-d90a92888432/contoso_roasters_20241025.xlsx) - this is a production log of coffee roasting
 
 Let's explore the data contained in these spreadsheets.  The **retail inventory sample** data contains data on the stocked products, their location in the store, and the amount of product sold for each day.  The fields representing stock location (e.g. stock_location_1, stock_location_2, etc.) are intended to convey the specific location in the store where a product was placed.

docs/azure_jumpstart_ag/contoso_hypermarket/shopper_insights/_index.md

@@ -83,7 +83,7 @@ Video inference is handled by two APIs using a common pattern.
 
 Person re-identification is a critical computer vision task that involves recognizing and tracking the same individual across different camera views or time periods. The following models are used in the footfall and shopper insights APIs.
 
-- Yolo8 Detection Model - [yolov8n.pt]([https://docs.openvino.ai/2022.3/omz_models_model_person_detection_retail_0013.html](https://docs.ultralytics.com/models/yolov8/#supported-tasks-and-modes))
+- Yolo8 Detection Model - [yolov8n.pt](https://docs.ultralytics.com/models/yolov8)
   - Detects people in video frames
   - Generates unique feature vectors for tracked individuals
   - Tracks people movement and time in zones