Merge branch 'main' of https://github.com/allegroai/clearml-docs into bot_usecase

docs/getting_started/clearml_agent_docker_exec.md

@@ -1,5 +1,5 @@
 ---
-title:   Building Executable Task Containers
+title: Building Executable Task Containers
 ---
 
 ## Exporting a Task into a Standalone Docker Container

docs/getting_started/clearml_agent_scheduling.md

@@ -3,7 +3,7 @@ title: Managing Agent Work Schedules
 ---
 
 :::important Enterprise Feature
-This feature is available under the ClearML Enterprise plan.
+Agent work schedule management is available under the ClearML Enterprise plan.
 :::
 
 The Agent scheduler enables scheduling working hours for each Agent. During working hours, a worker will actively poll 

docs/getting_started/main.md

@@ -32,19 +32,19 @@ training, and deploying models at every scale on any AI infrastructure.
 <tbody>
   <tr>
     <td><a href="https://github.com/clearml/clearml/blob/master/docs/tutorials/Getting_Started_1_Experiment_Management.ipynb"><b>Step 1</b></a> - Experiment Management</td>
-    <td className="align-center"><a className="no-ext-icon" target="_blank" href="https://colab.research.google.com/github/allegroai/clearml/blob/master/docs/tutorials/Getting_Started_1_Experiment_Management.ipynb">
+    <td className="align-center"><a className="no-ext-icon" target="_blank" href="https://colab.research.google.com/github/clearml/clearml/blob/master/docs/tutorials/Getting_Started_1_Experiment_Management.ipynb">
   <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
 </a></td>
   </tr>
   <tr>
     <td><a href="https://github.com/clearml/clearml/blob/master/docs/tutorials/Getting_Started_2_Setting_Up_Agent.ipynb"><b>Step 2</b></a> - Remote Execution Agent Setup</td>
-    <td className="align-center"><a className="no-ext-icon" target="_blank" href="https://colab.research.google.com/github/allegroai/clearml/blob/master/docs/tutorials/Getting_Started_2_Setting_Up_Agent.ipynb">
+    <td className="align-center"><a className="no-ext-icon" target="_blank" href="https://colab.research.google.com/github/clearml/clearml/blob/master/docs/tutorials/Getting_Started_2_Setting_Up_Agent.ipynb">
   <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
 </a></td>
   </tr>
   <tr>
     <td><a href="https://github.com/clearml/clearml/blob/master/docs/tutorials/Getting_Started_3_Remote_Execution.ipynb"><b>Step 3</b></a> - Remotely Execute Tasks</td>
-    <td className="align-center"><a className="no-ext-icon" target="_blank" href="https://colab.research.google.com/github/allegroai/clearml/blob/master/docs/tutorials/Getting_Started_3_Remote_Execution.ipynb">
+    <td className="align-center"><a className="no-ext-icon" target="_blank" href="https://colab.research.google.com/github/clearml/clearml/blob/master/docs/tutorials/Getting_Started_3_Remote_Execution.ipynb">
   <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
 </a></td>
   </tr>

docs/getting_started/remote_execution.md

@@ -14,7 +14,7 @@ powerful remote machine. This is useful for:
 * Managing execution through ClearML's queue system.
 
 This guide focuses on transitioning a locally executed process to a remote machine for scalable execution. To learn how 
-to reproduce a previously executed process on a remote machine, see [Reproducing Tasks](reproduce_tasks.md).
+to reproduce a previously executed process on a remote machine, see [Reproducing Task Runs](reproduce_tasks.md).
 
 ## Running a Task Remotely 
 

docs/getting_started/reproduce_tasks.md

@@ -1,5 +1,5 @@
 ---
-title: Reproducing Tasks 
+title: Reproducing Task Runs 
 ---
 
 :::note

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.