[DOCS] Ref sweep - DLS (#2349)

wiwaszko-intel · web-flow · commit 4fcb33e1bf81 · 2026-03-30T09:47:31.000+02:00
diff --git a/manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/pallet-defect-detection/how-to-guides/use-npu-for-inference.md b/manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/pallet-defect-detection/how-to-guides/use-npu-for-inference.md
@@ -42,11 +42,11 @@ Gstreamer has a variety of hardware specific encoders and decoders elements such
 
 Additionally, one can also enforce zero-copy of buffers using GStreamer caps (capabilities) to the pipeline by adding `video/x-raw(memory: VAMemory)` for Intel NPUs.
 
-Read DL Streamer [docs](https://dlstreamer.github.io/dev_guide/gpu_device_selection.html) for more details.
+Read DL Streamer [docs](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/dev_guide/gpu_device_selection.html) for more details.
 
 ### NPU specific element properties
 
-DL Streamer inference elements also provides property such as `device=NPU` and `pre-process-backend=va` which should be used in pipelines with NPU memory. It performs mapping to the system memory and uses VA pre-processor. Read DL Streamer [docs](https://dlstreamer.github.io/dev_guide/model_preparation.html#model-pre-and-post-processing) for more.
+DL Streamer inference elements also provides property such as `device=NPU` and `pre-process-backend=va` which should be used in pipelines with NPU memory. It performs mapping to the system memory and uses VA pre-processor. Read DL Streamer [docs](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/dev_guide/model_preparation.html#model-pre-and-post-processing) for more.
 
 ## Tutorial on how to use NPU specific pipelines
 
diff --git a/manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/pallet-defect-detection/troubleshooting.md b/manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/pallet-defect-detection/troubleshooting.md
@@ -130,7 +130,7 @@ There are few options to fix this.
 
 One is updating the kernel to `6.11.11-061111-generic` in the host system.
 
-Alternatively, install OpenCL runtime packages in the host system. Refer to the relevant [OpenVINO documentation](https://docs.openvino.ai/2025/get-started/install-openvino/configurations/configurations-intel-gpu.html#linux) to install GPU drivers.
+Alternatively, install OpenCL runtime packages in the host system. Refer to the relevant [OpenVINO documentation](https://docs.openvino.ai/2026/get-started/install-openvino/configurations/configurations-intel-gpu.html#linux) to install GPU drivers.
 
 ## Deploying on Edge Microvisor Toolkit
 
diff --git a/manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/pcb-anomaly-detection/how-to-guides/use-gpu-for-inference.md b/manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/pcb-anomaly-detection/how-to-guides/use-gpu-for-inference.md
@@ -32,15 +32,15 @@ Gstreamer has a variety of hardware specific encoders and decoders elements such
 
 Additionally, one can also enforce zero-copy of buffers using GStreamer caps (capabilities) to the pipeline by adding `video/x-raw(memory: VAMemory)` for Intel GPUs (integrated and discrete).
 
-Read DL Streamer [docs](https://dlstreamer.github.io/dev_guide/gpu_device_selection.html) for more details.
+Read DL Streamer [docs](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/dev_guide/gpu_device_selection.html) for more details.
 
 ### GPU specific element properties
 
-DL Streamer inference elements also provides property such as `device=GPU` and `pre-process-backend=va-surface-sharing` to infer and pre-process on GPU. Read DL Streamer [docs](https://dlstreamer.github.io/dev_guide/model_preparation.html#model-pre-and-post-processing) for more.
+DL Streamer inference elements also provides property such as `device=GPU` and `pre-process-backend=va-surface-sharing` to infer and pre-process on GPU. Read DL Streamer [docs](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/dev_guide/model_preparation.html#model-pre-and-post-processing) for more.
 
 ### Selecting the GPU render device of your choice if there is more than one GPU device on the system
 
-If you have multiple GPUs (integrated/discrete), please follow [this](https://dlstreamer.github.io/dev_guide/gpu_device_selection.html) DL Streamer document for selecting the GPU render device of your choice for VA codecs plugins.
+If you have multiple GPUs (integrated/discrete), please follow [this](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/dev_guide/gpu_device_selection.html) DL Streamer document for selecting the GPU render device of your choice for VA codecs plugins.
 
 ## Tutorial on how to use GPU specific pipelines
 
diff --git a/manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/pcb-anomaly-detection/how-to-guides/use-npu-for-inference.md b/manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/pcb-anomaly-detection/how-to-guides/use-npu-for-inference.md
@@ -42,11 +42,11 @@ Gstreamer has a variety of hardware specific encoders and decoders elements such
 
 Additionally, one can also enforce zero-copy of buffers using GStreamer caps (capabilities) to the pipeline by adding `video/x-raw(memory: VAMemory)` for Intel NPUs.
 
-Read DL Streamer [docs](https://dlstreamer.github.io/dev_guide/gpu_device_selection.html) for more details.
+Read DL Streamer [docs](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/dev_guide/gpu_device_selection.html) for more details.
 
 ### NPU specific element properties
 
-DL Streamer inference elements also provides property such as `device=NPU` and `pre-process-backend=va` which should be used in pipelines with NPU memory. It performs mapping to the system memory and uses VA pre-processor. Read DL Streamer [docs](https://dlstreamer.github.io/dev_guide/model_preparation.html#model-pre-and-post-processing) for more.
+DL Streamer inference elements also provides property such as `device=NPU` and `pre-process-backend=va` which should be used in pipelines with NPU memory. It performs mapping to the system memory and uses VA pre-processor. Read DL Streamer [docs](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/dev_guide/model_preparation.html#model-pre-and-post-processing) for more.
 
 ## Tutorial on how to use NPU specific pipelines
 
diff --git a/manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/pcb-anomaly-detection/troubleshooting.md b/manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/pcb-anomaly-detection/troubleshooting.md
@@ -91,7 +91,7 @@ privileged_access_required: true
 
 ## Inferencing on NPU
 
-To perform inferencing on an NPU device (for platforms with NPU accelerators such as Ultra Core processors), ensure you have completed the required pre-requisites. Refer to the instructions [here](https://dlstreamer.github.io/dev_guide/advanced_install/advanced_install_guide_prerequisites.html#prerequisite-2-install-intel-npu-drivers) to install Intel NPU drivers.
+To perform inferencing on an NPU device (for platforms with NPU accelerators such as Ultra Core processors), ensure you have completed the required pre-requisites. Refer to the instructions [here](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/dev_guide/advanced_install/advanced_install_guide_prerequisites.html#optional-prerequisite-2-install-intel-npu-drivers) to install Intel NPU drivers.
 
 ---
 
@@ -120,7 +120,7 @@ There are few options to fix this.
 
 One is updating the kernel to `6.11.11-061111-generic` in the host system.
 
-Alternatively, install OpenCL runtime packages in the host system. Refer to the instructions from OpenVINO documentation [here](https://docs.openvino.ai/2025/get-started/install-openvino/configurations/configurations-intel-gpu.html#linux) to install GPU drivers.
+Alternatively, install OpenCL runtime packages in the host system. Refer to the instructions from OpenVINO documentation [here](https://docs.openvino.ai/2026/get-started/install-openvino/configurations/configurations-intel-gpu.html#linux) to install GPU drivers.
 
 ## Deploying on Edge Microvisor Toolkit
 
diff --git a/manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/weld-porosity/how-to-guides/use-npu-for-inference.md b/manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/weld-porosity/how-to-guides/use-npu-for-inference.md
@@ -42,11 +42,11 @@ Gstreamer has a variety of hardware specific encoders and decoders elements such
 
 Additionally, one can also enforce zero-copy of buffers using GStreamer caps (capabilities) to the pipeline by adding `video/x-raw(memory: VAMemory)` for Intel NPUs.
 
-Read DL Streamer [docs](https://dlstreamer.github.io/dev_guide/gpu_device_selection.html) for more details.
+Read DL Streamer [docs](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/dev_guide/gpu_device_selection.html) for more details.
 
 ### NPU specific element properties
 
-DL Streamer inference elements also provides property such as `device=NPU` and `pre-process-backend=va` which should be used in pipelines with NPU memory. It performs mapping to the system memory and uses VA pre-processor. Read DL Streamer [docs](https://dlstreamer.github.io/dev_guide/model_preparation.html#model-pre-and-post-processing) for more.
+DL Streamer inference elements also provides property such as `device=NPU` and `pre-process-backend=va` which should be used in pipelines with NPU memory. It performs mapping to the system memory and uses VA pre-processor. Read DL Streamer [docs](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/dev_guide/model_preparation.html#model-pre-and-post-processing) for more.
 
 ## Tutorial on how to use NPU specific pipelines
 
diff --git a/manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/weld-porosity/troubleshooting.md b/manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/weld-porosity/troubleshooting.md
@@ -117,7 +117,7 @@ There are few options to fix this.
 
 One is updating the kernel to `6.11.11-061111-generic` in the host system.
 
-Alternatively, install OpenCL runtime packages in the host system. Refer to the instructions from OpenVINO documentation [here](https://docs.openvino.ai/2025/get-started/install-openvino/configurations/configurations-intel-gpu.html#linux) to install GPU drivers.
+Alternatively, install OpenCL runtime packages in the host system. Refer to the instructions from OpenVINO documentation [here](https://docs.openvino.ai/2026/get-started/install-openvino/configurations/configurations-intel-gpu.html#linux) to install GPU drivers.
 
 ## Deploying on Edge Microvisor Toolkit
 
diff --git a/manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/worker-safety-gear-detection/get-started.md b/manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/worker-safety-gear-detection/get-started.md
@@ -7,7 +7,7 @@
 
 - [System Requirements](./get-started/system-requirements.md)
 
-## Setup the application
+## Set up the application
 
 The following instructions assume Docker engine is correctly set up in the host system.
 If not, follow the [installation guide for docker engine](https://docs.docker.com/engine/install/ubuntu/).
diff --git a/manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/worker-safety-gear-detection/how-to-guides/use-npu-for-inference.md b/manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/worker-safety-gear-detection/how-to-guides/use-npu-for-inference.md
@@ -42,11 +42,11 @@ Gstreamer has a variety of hardware specific encoders and decoders elements such
 
 Additionally, one can also enforce zero-copy of buffers using GStreamer caps (capabilities) to the pipeline by adding `video/x-raw(memory: VAMemory)` for Intel NPUs.
 
-Read DL Streamer [docs](https://dlstreamer.github.io/dev_guide/gpu_device_selection.html) for more details.
+Read DL Streamer [docs](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/dev_guide/gpu_device_selection.html) for more details.
 
 ### NPU specific element properties
 
-DL Streamer inference elements also provides property such as `device=NPU` and `pre-process-backend=va` which should be used in pipelines with NPU memory. It performs mapping to the system memory and uses VA pre-processor. Read DL Streamer [docs](https://dlstreamer.github.io/dev_guide/model_preparation.html#model-pre-and-post-processing) for more.
+DL Streamer inference elements also provides property such as `device=NPU` and `pre-process-backend=va` which should be used in pipelines with NPU memory. It performs mapping to the system memory and uses VA pre-processor. Read DL Streamer [docs](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/dev_guide/model_preparation.html#model-pre-and-post-processing) for more.
 
 ## Tutorial on how to use NPU specific pipelines
 
diff --git a/manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/worker-safety-gear-detection/troubleshooting.md b/manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/worker-safety-gear-detection/troubleshooting.md
@@ -128,7 +128,7 @@ There are few options to fix this.
 
 One is updating the kernel to `6.11.11-061111-generic` in the host system.
 
-Alternatively, install OpenCL runtime packages in the host system. Refer to the instructions from [OpenVINO documentation](https://docs.openvino.ai/2025/get-started/install-openvino/configurations/configurations-intel-gpu.html#linux) to install GPU drivers.
+Alternatively, install OpenCL runtime packages in the host system. Refer to the instructions from [OpenVINO documentation](https://docs.openvino.ai/2026/get-started/install-openvino/configurations/configurations-intel-gpu.html#linux) to install GPU drivers.
 
 ## Deploying on Edge Microvisor Toolkit
 
diff --git a/metro-ai-suite/live-video-analysis/live-video-captioning/docs/user-guide/object-detection-pipeline.md b/metro-ai-suite/live-video-analysis/live-video-captioning/docs/user-guide/object-detection-pipeline.md
@@ -21,7 +21,7 @@ User can enable object detection in the pipeline by following the steps below:
      CAPTION_HISTORY=3
      ```
 
-2. Prepare the object-detection models by using this [script](https://github.com/open-edge-platform/dlstreamer/blob/master/samples/download_public_models.sh).
+2. Prepare the object-detection models by using this [script](https://github.com/open-edge-platform/dlstreamer/blob/main/samples/download_public_models.sh).
      ```bash
      # Navigate to the directory
      cd edge-ai-suites/metro-ai-suite/live-video-analysis/live-video-captioning
@@ -30,7 +30,7 @@ User can enable object detection in the pipeline by following the steps below:
      sudo rm -rf ov_detection_models && mkdir ov_detection_models
 
      # Download the script
-     curl -O https://raw.githubusercontent.com/open-edge-platform/dlstreamer/master/samples/download_public_models.sh && chmod +x download_public_models.sh
+     curl -O https://raw.githubusercontent.com/open-edge-platform/dlstreamer/main/samples/download_public_models.sh && chmod +x download_public_models.sh
 
      # Export the MODELS_PATH to store the detection model files downloaded. For example: `yolov8s`
      export MODELS_PATH=${PWD}/ov_detection_models/yolov8s
diff --git a/metro-ai-suite/metro-sdk-manager/docs/user-guide/metro-vision-ai-sdk/get-started.md b/metro-ai-suite/metro-sdk-manager/docs/user-guide/metro-vision-ai-sdk/get-started.md
@@ -60,7 +60,7 @@ wget -O sample.mp4 https://github.com/intel-iot-devkit/sample-videos/raw/master/
 mkdir -p models/intel/pedestrian-and-vehicle-detector-adas-0001/FP32/
 wget -O "models/intel/pedestrian-and-vehicle-detector-adas-0001/FP32/pedestrian-and-vehicle-detector-adas-0001.xml" "https://storage.openvinotoolkit.org/repositories/open_model_zoo/2023.0/models_bin/1/pedestrian-and-vehicle-detector-adas-0001/FP32/pedestrian-and-vehicle-detector-adas-0001.xml?raw=true"
 wget -O "models/intel/pedestrian-and-vehicle-detector-adas-0001/FP32/pedestrian-and-vehicle-detector-adas-0001.bin" "https://storage.openvinotoolkit.org/repositories/open_model_zoo/2023.0/models_bin/1/pedestrian-and-vehicle-detector-adas-0001/FP32/pedestrian-and-vehicle-detector-adas-0001.bin?raw=true"
-wget -O "models/intel/pedestrian-and-vehicle-detector-adas-0001/pedestrian-and-vehicle-detector-adas-0001.json" "https://raw.githubusercontent.com/dlstreamer/dlstreamer/refs/heads/main/samples/gstreamer/model_proc/intel/pedestrian-and-vehicle-detector-adas-0001.json"
+wget -O "models/intel/pedestrian-and-vehicle-detector-adas-0001/pedestrian-and-vehicle-detector-adas-0001.json" "https://raw.githubusercontent.com/open-edge-platform/dlstreamer/refs/heads/main/samples/gstreamer/model_proc/intel/pedestrian-and-vehicle-detector-adas-0001.json"
 ```
 
 ### Step 3: Pipeline Execution
diff --git a/metro-ai-suite/metro-sdk-manager/docs/user-guide/metro-vision-ai-sdk/tutorial-4.md b/metro-ai-suite/metro-sdk-manager/docs/user-guide/metro-vision-ai-sdk/tutorial-4.md
@@ -76,7 +76,7 @@ wget -O models/intel/human-pose-estimation-0001/FP32/human-pose-estimation-0001.
   "https://storage.openvinotoolkit.org/repositories/open_model_zoo/2023.0/models_bin/1/human-pose-estimation-0001/FP32/human-pose-estimation-0001.bin"
 
 wget -O models/intel/human-pose-estimation-0001/human-pose-estimation-0001.json \
-  "https://raw.githubusercontent.com/dlstreamer/dlstreamer/main/samples/gstreamer/model_proc/intel/human-pose-estimation-0001.json"
+  "https://raw.githubusercontent.com/open-edge-platform/dlstreamer/main/samples/gstreamer/model_proc/intel/human-pose-estimation-0001.json"
 ```
 
 ### Step 2: Understand Human Pose Estimation Model
diff --git a/metro-ai-suite/metro-sdk-manager/docs/user-guide/visual-ai-demo-kit/get-started.md b/metro-ai-suite/metro-sdk-manager/docs/user-guide/visual-ai-demo-kit/get-started.md
@@ -179,9 +179,9 @@ Create compelling visualization experiences for your AI applications. This tutor
   \- Comprehensive documentation for Intel's GStreamer-based video analytics framework
 - [DL Streamer Pipeline Server](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer-pipeline-server/index.html)
   \- RESTful microservice architecture documentation for scalable video analytics deployment
-- [OpenVINO](https://docs.openvino.ai/2025/get-started.html)
+- [OpenVINO](https://docs.openvino.ai/2026/get-started.html)
   \- Complete reference for Intel's cross-platform inference optimization toolkit
-- [OpenVINO Model Server](https://docs.openvino.ai/2025/model-server/ovms_what_is_openvino_model_server.html)
+- [OpenVINO Model Server](https://docs.openvino.ai/2026/model-server/ovms_what_is_openvino_model_server.html)
   \- Model serving infrastructure documentation for production deployments
 - [Edge AI Libraries](https://docs.openedgeplatform.intel.com/dev/ai-libraries.html)
   \- Comprehensive development toolkit documentation and API references
diff --git a/metro-ai-suite/metro-sdk-manager/docs/user-guide/visual-ai-demo-kit/tutorial-1.md b/metro-ai-suite/metro-sdk-manager/docs/user-guide/visual-ai-demo-kit/tutorial-1.md
@@ -91,20 +91,20 @@ cd /home/dlstreamer/metro-suite/
 
 mkdir -p ai-tolling/src/dlstreamer-pipeline-server/models/public
 export MODELS_PATH=/home/dlstreamer/metro-suite/ai-tolling/src/dlstreamer-pipeline-server/models
-/home/dlstreamer/dlstreamer/samples/download_public_models.sh yolov10s
+/home/open-edge-platform/dlstreamer/samples/download_public_models.sh yolov10s
 
 mkdir -p ai-tolling/src/dlstreamer-pipeline-server/models/intel
 
 python3 -m pip install openvino-dev[onnx,tensorflow2]
 
 omz_downloader --name license-plate-recognition-barrier-0007 -o /home/dlstreamer/metro-suite/ai-tolling/src/dlstreamer-pipeline-server/models/
 omz_converter --name license-plate-recognition-barrier-0007  -o /home/dlstreamer/metro-suite/ai-tolling/src/dlstreamer-pipeline-server/models/ -d /home/dlstreamer/metro-suite/ai-tolling/src/dlstreamer-pipeline-server/models/
-wget -O "/home/dlstreamer/metro-suite/ai-tolling/src/dlstreamer-pipeline-server/models/public/license-plate-recognition-barrier-0007/license-plate-recognition-barrier-0007.json" "https://raw.githubusercontent.com/dlstreamer/dlstreamer/refs/heads/main/samples/gstreamer/model_proc/intel/license-plate-recognition-barrier-0007.json"
+wget -O "/home/dlstreamer/metro-suite/ai-tolling/src/dlstreamer-pipeline-server/models/public/license-plate-recognition-barrier-0007/license-plate-recognition-barrier-0007.json" "https://raw.githubusercontent.com/open-edge-platform/dlstreamer/refs/heads/main/samples/gstreamer/model_proc/intel/license-plate-recognition-barrier-0007.json"
 
 
 omz_downloader --name vehicle-attributes-recognition-barrier-0039 -o /home/dlstreamer/metro-suite/ai-tolling/src/dlstreamer-pipeline-server/models/
 omz_converter --name  vehicle-attributes-recognition-barrier-0039 -o /home/dlstreamer/metro-suite/ai-tolling/src/dlstreamer-pipeline-server/models/ -d /home/dlstreamer/metro-suite/ai-tolling/src/dlstreamer-pipeline-server/models/
-wget -O "/home/dlstreamer/metro-suite/ai-tolling/src/dlstreamer-pipeline-server/models/intel/vehicle-attributes-recognition-barrier-0039/vehicle-attributes-recognition-barrier-0039.json" "https://raw.githubusercontent.com/dlstreamer/dlstreamer/refs/heads/main/samples/gstreamer/model_proc/intel/vehicle-attributes-recognition-barrier-0039.json"
+wget -O "/home/dlstreamer/metro-suite/ai-tolling/src/dlstreamer-pipeline-server/models/intel/vehicle-attributes-recognition-barrier-0039/vehicle-attributes-recognition-barrier-0039.json" "https://raw.githubusercontent.com/open-edge-platform/dlstreamer/refs/heads/main/samples/gstreamer/model_proc/intel/vehicle-attributes-recognition-barrier-0039.json"
 
 echo "Fix ownership..."
 chown -R "$(id -u):$(id -g)" ai-tolling/src/dlstreamer-pipeline-server/models ai-tolling/src/dlstreamer-pipeline-server/videos 2>/dev/null || true
@@ -398,8 +398,7 @@ After successfully setting up the AI Tolling system, consider these enhancements
 
 [**Integration with Node Red for enhancing business logic**](./tutorial-2.md)
 
-
 ## Supporting Resources
 
-- [DL Streamer Documentation](https://dlstreamer.github.io/)
+- [DL Streamer Documentation](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/index.html)
 - [Metro AI Solutions](https://github.com/open-edge-platform/edge-ai-suites/tree/main/metro-ai-suite)
diff --git a/metro-ai-suite/metro-sdk-manager/docs/user-guide/visual-ai-demo-kit/tutorial-2.md b/metro-ai-suite/metro-sdk-manager/docs/user-guide/visual-ai-demo-kit/tutorial-2.md
@@ -287,5 +287,5 @@ After successfully setting up the AI Tolling system with Node Red, consider thes
 
 - [Node-RED Official Documentation](https://nodered.org/docs/)
 - [MQTT Protocol Specification](https://mqtt.org/)
-- [DL Streamer Documentation](https://dlstreamer.github.io/)
+- [DL Streamer Documentation](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/index.html)
 - [Metro AI Solutions](https://github.com/open-edge-platform/edge-ai-suites/tree/main/metro-ai-suite)
diff --git a/metro-ai-suite/metro-vision-ai-app-recipe/docs/user-guide/tutorial-1.md b/metro-ai-suite/metro-vision-ai-app-recipe/docs/user-guide/tutorial-1.md
diff --git a/metro-ai-suite/metro-vision-ai-app-recipe/docs/user-guide/tutorial-2.md b/metro-ai-suite/metro-vision-ai-app-recipe/docs/user-guide/tutorial-2.md
diff --git a/metro-ai-suite/metro-vision-ai-app-recipe/docs/user-guide/tutorial-4.md b/metro-ai-suite/metro-vision-ai-app-recipe/docs/user-guide/tutorial-4.md
diff --git a/metro-ai-suite/sensor-fusion-for-traffic-management/docs/user-guide/get-started-guide.md b/metro-ai-suite/sensor-fusion-for-traffic-management/docs/user-guide/get-started-guide.md
diff --git a/robotics-ai-suite/docs/embodied/developer_tools_tutorials/model_tutorials/model_dp.rst b/robotics-ai-suite/docs/embodied/developer_tools_tutorials/model_tutorials/model_dp.rst
diff --git a/robotics-ai-suite/docs/embodied/developer_tools_tutorials/model_tutorials/model_idp3.rst b/robotics-ai-suite/docs/embodied/developer_tools_tutorials/model_tutorials/model_idp3.rst
