Small edits

docs/clearml_serving/clearml_serving_tutorial.md

@@ -212,7 +212,7 @@ Example:
 ClearML serving instances send serving statistics (count/latency) automatically to Prometheus and Grafana can be used 
 to visualize and create live dashboards. 
 
-The default docker-compose installation is preconfigured with Prometheus and Grafana. Notice that by default data/ate 
+The default `docker-compose` installation is preconfigured with Prometheus and Grafana. Notice that by default data/ate 
 of both containers is *not* persistent. To add persistence, adding a volume mount is recommended.
 
 You can also add many custom metrics on the input/predictions of your models. Once a model endpoint is registered, 

docs/deploying_clearml/clearml_server_linux_mac.md

@@ -5,8 +5,8 @@ title: Linux and macOS
 Deploy the ClearML Server in Linux or macOS using the pre-built Docker image.
 
 For ClearML docker images, including previous versions, see [https://hub.docker.com/r/allegroai/clearml](https://hub.docker.com/r/allegroai/clearml). 
-However, pulling the ClearML Docker image directly is not required. ClearML provides a docker-compose YAML file that does this. 
-The docker-compose file is included in the instructions on this page.
+However, pulling the ClearML Docker image directly is not required. ClearML provides a `docker-compose` YAML file that does this. 
+The `docker-compose` file is included in the instructions on this page.
 
 For information about upgrading ClearML Server in Linux or macOS, see [here](upgrade_server_linux_mac.md).
 
@@ -134,7 +134,7 @@ Deploying the server requires a minimum of 8 GB of memory, 16 GB is recommended.
        sudo chown -R $(whoami):staff /opt/clearml
        ```
 
-2. Download the ClearML Server docker-compose YAML file.
+2. Download the ClearML Server `docker-compose` YAML file.
       ```
       sudo curl https://raw.githubusercontent.com/clearml/clearml-server/master/docker/docker-compose.yml -o /opt/clearml/docker-compose.yml
       ```

docs/deploying_clearml/clearml_server_win.md

@@ -54,7 +54,7 @@ Deploying the server requires a minimum of 8 GB of memory, 16 GB is recommended.
    mkdir c:\opt\clearml\logs
    ```
 
-1. Save the ClearML Server docker-compose YAML file.
+1. Save the ClearML Server `docker-compose` YAML file.
  
    ```   
    curl https://raw.githubusercontent.com/clearml/clearml-server/master/docker/docker-compose-win10.yml -o c:\opt\clearml\docker-compose-win10.yml

docs/deploying_clearml/enterprise_deploy/app_install_ubuntu_on_prem.md

@@ -13,7 +13,7 @@ without any coding. Applications are installed on top of the ClearML Server.
 To run application you will need the following:
 * RAM: Make sure you have at least 400 MB of RAM per application instance.
 * Applications Service: Make sure that the applications agent service is up and running on your server:
-  * If you are using a docker-compose solution, make sure that the clearml-apps-agent service is running.
+  * If you are using a `docker-compose` solution, make sure that the clearml-apps-agent service is running.
   * If you are using a Kubernetes cluster, check for the clearml-clearml-enterprise-apps component.
 * Installation Files: Each application has its installation zip file. Make sure you have the relevant files for the 
 applications you wish to install.

docs/deploying_clearml/enterprise_deploy/import_projects.md

@@ -36,7 +36,7 @@ them before exporting.
 Execute the data tool within the `apiserver` container.
 
 Open a bash session inside the `apiserver` container of the server:  
-* In docker-compose:
+* In `docker-compose`:
   
   ```commandline
   sudo docker exec -it clearml-apiserver /bin/bash

docs/deploying_clearml/enterprise_deploy/on_prem_ubuntu.md

@@ -165,9 +165,12 @@ should be reviewed and modified prior to the server installation
    sudo docker login -u=$DOCKERHUB_USER -p=$DOCKERHUB_PASSWORD
    ```
    
-1. Start the `docker-compose`  by changing directories to the directory containing the docker-compose files and running the following command:
-sudo docker-compose --env-file constants.env up -d
-
+1. Start the `docker-compose`  by changing directories to the directory containing the `docker-compose` files and running the following command:
+   
+   ```
+   sudo docker-compose --env-file constants.env up -d
+   ```
+   
 1. Verify web access by browsing to your URL (IP address) and port 8080.
 
    ```

docs/deploying_clearml/enterprise_deploy/vpc_aws.md

@@ -8,7 +8,7 @@ It covers the following:
 * Set up security groups and IAM role
 * Create EC2 instance with required disks
 * Install dependencies and mount disks
-* Deploy ClearML version using docker-compose
+* Deploy ClearML version using `docker-compose`
 * Set up load balancer and DNS
 * Set up server backup 
 

docs/getting_started/video_tutorials/core_component_overview.md

@@ -31,7 +31,7 @@ The pip package also includes `clearml-data`. It can help you keep track of your
 
 Both the 2 magic lines and the data tool will send all of their information to a ClearML server. This server then keeps an overview of your experiment runs and data sets over time, so you can always go back to a previous experiment, see how it was created and even recreate it exactly. Keep track of your best models by creating leaderboards based on your own metrics, and you can even directly compare multiple experiment runs, helping you to figure out the best way forward for your models. 
 
-To get started with a server right away, you can make use of the free tier. And when your needs grow, we've got you covered too! Just check out our website to find a tier that fits your organisation best. But, because we're open source, you can also host your own completely for free. We have AWS images, Google Cloud images, you can run it on docker-compose locally or even, if you really hate yourself, run it on a self-hosted kubernetes cluster using our helm charts.
+To get started with a server right away, you can make use of the free tier. And when your needs grow, we've got you covered too! Just check out our website to find a tier that fits your organisation best. But, because we're open source, you can also host your own completely for free. We have AWS images, Google Cloud images, you can run it on `docker-compose` locally or even, if you really hate yourself, run it on a self-hosted kubernetes cluster using our helm charts.
 
 So, to recap: to get started, all you need is a pip package and a server to store everything. Easy right? But MLOps is much more than experiment and data management. It's also about automation and orchestration, which is exactly where the `clearml-agent` comes into play.