diff --git a/docs/clearml_sdk/task_sdk.md b/docs/clearml_sdk/task_sdk.md
index 58a931b8..a26763f1 100644
--- a/docs/clearml_sdk/task_sdk.md
+++ b/docs/clearml_sdk/task_sdk.md
@@ -831,7 +831,7 @@ task = Task.init(project_name='examples', task_name='parameters')
 task.set_parameters({'Args/epochs':7, 'lr': 0.5})
 
 # setting a single parameter
-task.set_parameter(name='decay',value=0.001)
+task.set_parameter(name='decay', value=0.001)
 ```
 
 :::warning Overwriting Parameters
@@ -889,7 +889,7 @@ me = Person('Erik', 5)
 
 params_dictionary = {'epochs': 3, 'lr': 0.4}
 
-task = Task.init(project_name='examples',task_name='python objects')
+task = Task.init(project_name='examples', task_name='python objects')
 
 task.connect(me)
 task.connect(params_dictionary)
diff --git a/docs/clearml_serving/clearml_serving_tutorial.md b/docs/clearml_serving/clearml_serving_tutorial.md
index f7cf1d93..d6e383d6 100644
--- a/docs/clearml_serving/clearml_serving_tutorial.md
+++ b/docs/clearml_serving/clearml_serving_tutorial.md
@@ -38,13 +38,13 @@ clearml-serving --id <service_id> model add --engine sklearn --endpoint "test_mo
 :::info Service ID
 Make sure that you have executed `clearml-serving`'s
 [initial setup](clearml_serving_setup.md#initial-setup), in which you create a Serving Service. 
-The Serving Service's ID is required to register a model, and to execute `clearml-serving`'s `metrics` and `config` commands 
+The Serving Service's ID is required to register a model, and to execute `clearml-serving`'s `metrics` and `config` commands. 
 :::
 
   
 :::note
 The preprocessing Python code is packaged and uploaded to the Serving Service, to be used by any inference container, 
-and downloaded in real time when updated
+and downloaded in real time when updated.
 :::
 
 ### Step 3: Spin Inference Container
@@ -110,7 +110,7 @@ or with the `clearml-serving` CLI.
 You can also provide a different storage destination for the model, such as S3/GS/Azure, by passing
 `--destination="s3://bucket/folder"`, `s3://host_addr:port/bucket` (for non-AWS S3-like services like MinIO), `gs://bucket/folder`, `azure://<account name>.blob.core.windows.net/path/to/file`. There is no need to provide a unique 
 path to the destination argument, the location of the model will be a unique path based on the serving service ID and the 
-model name
+model name.
 :::
 
 ## Additional Options 
@@ -160,7 +160,7 @@ This means that any request coming to `/test_model_sklearn_canary/` will be rout
 
 :::note
 As with any other Serving Service configuration, you can configure the Canary endpoint while the Inference containers are 
-already running and deployed, they will get updated in their next update cycle (default: once every 5 minutes)
+already running and deployed, they will get updated in their next update cycle (default: once every 5 minutes).
 :::
 
 You can also prepare a "fixed" canary endpoint, always splitting the load between the last two deployed models:
@@ -244,7 +244,7 @@ With the new metrics logged, you can create a visualization dashboard over the l
 :::note
 If not specified all serving requests will be logged, which can be changed with the `CLEARML_DEFAULT_METRIC_LOG_FREQ` 
 environment variable. For example `CLEARML_DEFAULT_METRIC_LOG_FREQ=0.2` means only 20% of all requests will be logged. 
-You can also specify per-endpoint log frequency with the `clearml-serving` CLI. See [clearml-serving metrics](clearml_serving_cli.md#metrics)
+You can also specify per-endpoint log frequency with the `clearml-serving` CLI. See [clearml-serving metrics](clearml_serving_cli.md#metrics).
 :::
 
 ## Further Examples
diff --git a/docs/configs/clearml_conf.md b/docs/configs/clearml_conf.md
index d7f86216..298f22a5 100644
--- a/docs/configs/clearml_conf.md
+++ b/docs/configs/clearml_conf.md
@@ -1107,8 +1107,8 @@ URL to a CA bundle, or set this option to `false` to skip SSL certificate verifi
 
 * Log specific environment variables. OS environments are listed in the UI, under an experiment's  
   **CONFIGURATION > HYPERPARAMETERS > Environment** section. 
-  Multiple selected variables are supported including the suffix "\*". For example: "AWS\_\*" will log any OS environment 
-  variable starting with `"AWS\_"`. Example: `log_os_environments: ["AWS_*", "CUDA_VERSION"]`
+  Multiple selected variables are supported including the suffix `*`. For example: `"AWS_*"` will log any OS environment 
+  variable starting with `"AWS_"`. Example: `log_os_environments: ["AWS_*", "CUDA_VERSION"]`
         
 * This value can be overwritten with OS environment variable `CLEARML_LOG_ENVIRONMENT=AWS_*,CUDA_VERSION`. 
 
diff --git a/docs/webapp/webapp_exp_track_visual.md b/docs/webapp/webapp_exp_track_visual.md
index 2d6e1012..2d95a2a5 100644
--- a/docs/webapp/webapp_exp_track_visual.md
+++ b/docs/webapp/webapp_exp_track_visual.md
@@ -199,6 +199,7 @@ The task's input and output models appear in the **ARTIFACTS** tab. Each model e
 * Model name
 * ID
 * Configuration. 
+
 Input models also display their creating experiment, which on-click navigates you to the experiment's page. 
 
 ![Models in Artifacts tab](../img/webapp_exp_artifacts_01.png)