diff --git a/docs/clearml_serving/clearml_serving_setup.md b/docs/clearml_serving/clearml_serving_setup.md
index 05219921..4820a946 100644
--- a/docs/clearml_serving/clearml_serving_setup.md
+++ b/docs/clearml_serving/clearml_serving_setup.md
@@ -15,7 +15,7 @@ The following page goes over how to set up and upgrade `clearml-serving`.
   [free hosted service](https://app.clear.ml)
 1. Connect `clearml` SDK to the server, see instructions [here](../clearml_sdk/clearml_sdk_setup#install-clearml)
 
-1. Install clearml-serving CLI:
+1. Install the `clearml-serving` CLI:
    
    ```bash
    pip3 install clearml-serving
@@ -27,21 +27,22 @@ The following page goes over how to set up and upgrade `clearml-serving`.
    clearml-serving create --name "serving example"
    ```
    
-   The new serving service UID should be printed 
+   This command prints the Serving Service UID:
    
    ```console
    New Serving Service created: id=aa11bb22aa11bb22
    ```
    
-   Write down the Serving Service UID
+   Copy the Serving Service UID (e.g., `aa11bb22aa11bb22`), as you will need it in the next steps.
 
 1. Clone the `clearml-serving` repository:
    ```bash
    git clone https://github.com/clearml/clearml-serving.git
    ```
 
-1. Edit the environment variables file (docker/example.env) with your clearml-server credentials and Serving Service UID. 
-   For example, you should have something like
+1. Edit the environment variables file (`docker/example.env`) with your `clearml-server` API credentials and Serving Service UID. 
+   For example:
+
    ```bash
    cat docker/example.env
    ```
@@ -55,31 +56,30 @@ The following page goes over how to set up and upgrade `clearml-serving`.
     CLEARML_SERVING_TASK_ID="<serving_service_id_here>"
    ```
 
-1. Spin up the `clearml-serving` containers with `docker-compose` (or if running on Kubernetes, use the helm chart)
+1. Spin up the `clearml-serving` containers with `docker-compose` (or if running on Kubernetes, use the helm chart):
    
    ```bash
    cd docker && docker-compose --env-file example.env -f docker-compose.yml up
    ```
     
-   If you need Triton support (keras/pytorch/onnx etc.), use the triton docker-compose file
+   If you need Triton support (Keras/PyTorch/ONNX etc.), use the triton `docker-compose` file:
    ```bash
    cd docker && docker-compose --env-file example.env -f docker-compose-triton.yml up 
    ```
    
-   If running on a GPU instance with Triton support (keras/pytorch/onnx etc.), use the triton gpu docker-compose file:
+   If running on a GPU instance with Triton support (Keras/PyTorch/ONNX etc.), use the triton gpu docker-compose file:
    ```bash
    cd docker && docker-compose --env-file example.env -f docker-compose-triton-gpu.yml up
    ```
     
 :::note
-Any model that registers with Triton engine will run the pre/post-processing code on the Inference service container, 
+Any model that registers with Triton engine will run the pre/post-processing code in the Inference service container, 
 and the model inference itself will be executed on the Triton Engine container.
 :::
 
 ## Advanced Setup - S3/GS/Azure Access (Optional)
-To add access credentials and allow the inference containers to download models from your S3/GS/Azure object-storage, 
-add the respective environment variables to your env files (example.env). For further details, see 
-[Configuring Storage](../integrations/storage.md#configuring-storage).
+To enable inference containers to download models from S3, Google Cloud Storage (GS), or Azure,
+add access credentials in the respective environment variables to your env files (`example.env`): 
 
 ```
 AWS_ACCESS_KEY_ID
@@ -92,14 +92,21 @@ AZURE_STORAGE_ACCOUNT
 AZURE_STORAGE_KEY
 ```
 
+For further details, see [Configuring Storage](../integrations/storage.md#configuring-storage).
+
 ## Upgrading ClearML Serving
 
 **Upgrading to v1.1**
 
-1. Take down the serving containers (`docker-compose` or k8s)
-1. Update the `clearml-serving` CLI `pip3 install -U clearml-serving`
+1. Shut down the serving containers (`docker-compose` or k8s)
+1. Update the `clearml-serving` CLI: 
+
+   ```
+   pip3 install -U clearml-serving
+   ```
+   
 1. Re-add a single existing endpoint with `clearml-serving model add ...` (press yes when asked). It will upgrade the 
-   `clearml-serving` session definitions
+   `clearml-serving` session definitions.
 1. Pull the latest serving containers (`docker-compose pull ...` or k8s)
 1. Re-spin serving containers (`docker-compose` or k8s)
 
diff --git a/docs/integrations/transformers.md b/docs/integrations/transformers.md
index c6f4ff07..9483eeaa 100644
--- a/docs/integrations/transformers.md
+++ b/docs/integrations/transformers.md
@@ -77,12 +77,12 @@ cloud of your choice (AWS, GCP, Azure) and automatically deploy ClearML agents:
 and shuts down instances as needed, according to a resource budget that you set.
 
 
-### Reproducing Tasks
+### Reproducing Task Runs
 
 ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only)
 ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only)
 
-Use ClearML's web interface to reproduce tasks and edit their details, like hyperparameters or input models, then execute the tasks 
+Use ClearML's web interface to reproduce task runs and edit their details, like hyperparameters or input models, then execute the tasks 
 with the new configuration on a remote machine.
 
 When ClearML is integrated into a script, it captures and stores configurations, such as hyperparameters