Add space between words

docs/azure_jumpstart_ag/manufacturing/contoso_motors/ai_inferencing/_index.md

@@ -56,7 +56,7 @@ The steps involved in the inference/procressing can be described as follows:
 
     The AI inference server, in our case the [OpenVINO™ Model Server](https://docs.openvino.ai/2023.3/ovms_what_is_openvino_model_server.html), hosts models and makes them accessible to software components over standard network protocols: a client sends a request to the model server, which performs model inference and sends a response back to the client.
 
-    The OVMS model server is configured as part of the helm deployment, using the **OVMS Operator**. The deployment installs the OVMS Operator, sets up the storage for AI models, and configures the OVMS pods and services. For more information about the setup, check [OVMS Helm](https://github.com/microsoft/jumpstart-agora-apps/tree/manufacturing/contoso_manufacturing/operations/charts/ovms).For more information about OVMS Operator, check [OpenVINO™ Model Server with Kubernetes](https://docs.openvino.ai/archive/2021.4/ovms_docs_kubernetes.html). The OVMS model server is typically deployed as a set of pods. Each pod contains one or more instances of the OVMS software, along with any necessary dependencies. The pods are managed by a Kubernetes controller, which is responsible for ensuring that the desired number of pods are running at all times.
+    The OVMS model server is configured as part of the helm deployment, using the **OVMS Operator**. The deployment installs the OVMS Operator, sets up the storage for AI models, and configures the OVMS pods and services. For more information about the setup, check [OVMS Helm](https://github.com/microsoft/jumpstart-agora-apps/tree/manufacturing/contoso_manufacturing/operations/charts/ovms). For more information about OVMS Operator, check [OpenVINO™ Model Server with Kubernetes](https://docs.openvino.ai/archive/2021.4/ovms_docs_kubernetes.html). The OVMS model server is typically deployed as a set of pods. Each pod contains one or more instances of the OVMS software, along with any necessary dependencies. The pods are managed by a Kubernetes controller, which is responsible for ensuring that the desired number of pods are running at all times.
 
 2. **Post-process and UI rendering:** this is the code in the **Web AI Inference and UI** pod is responsible for handling post-processing and final UI rendering for the user. Depending on the model, once the OVMS provides the inference response, post-processing is applied to the image, such as adding labels, bounding boxes, or human skeleton graphs. Once the visual data is added to the frame, it's then served to the UI frontend using a **Flask App** method. For more information on the Flask App, please refer to the [Web UI - app.py](https://github.com/microsoft/jumpstart-agora-apps/blob/main/contoso_manufacturing/developer/webapp-decode/app.py)